1. Presence/absence data
Aceroides aceroides latipes
## SDM for: aceroides_aceroides_latipes
Abiotic parameters
## McFadden's pseudo-R2 is: 0.22
## Tjur's pseudo-R2 is: 0.2
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-10.29 |
1158 |
-0.008887 |
0.9929 |
|
| om |
0.7006 |
0.5908 |
1.186 |
0.2357 |
|
| gravel |
-32.64 |
4133 |
-0.007899 |
0.9937 |
|
| silt |
-1.057 |
0.6364 |
-1.661 |
0.09664 |
|
| clay |
-0.4924 |
1.504 |
-0.3275 |
0.7433 |
|
| arsenic |
-0.5843 |
0.7725 |
-0.7564 |
0.4494 |
|
| cadmium |
-0.1074 |
0.4716 |
-0.2278 |
0.8198 |
|
| copper |
0.7994 |
0.68 |
1.176 |
0.2398 |
|
| iron |
-2.038 |
1.165 |
-1.75 |
0.08011 |
|
| manganese |
0.6651 |
0.727 |
0.9149 |
0.3602 |
|
| mercury |
0.5689 |
0.4664 |
1.22 |
0.2225 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.97 |
1 |
2.02 |
1.03 |
1.76 |
1.44 |
2.3 |
2.86 |
2.31 |
1.55 |

Influence indices
## McFadden's pseudo-R2 is: 0.36
## Tjur's pseudo-R2 is: 0.35
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.406 |
23.18 |
-0.1038 |
0.9173 |
|
| aquaculture |
-10.12 |
4.854 |
-2.085 |
0.0371 |
* |
| city |
-5.22 |
4.227 |
-1.235 |
0.2169 |
|
| dredging_collect |
1.978 |
3.279 |
0.6032 |
0.5464 |
|
| dredging_dump |
8.674 |
4.346 |
1.996 |
0.04595 |
* |
| industry |
4.368 |
2.179 |
2.004 |
0.04502 |
* |
| shipping_mooring |
-4.069 |
3.32 |
-1.226 |
0.2203 |
|
| shipping_traffic |
2.921 |
1.787 |
1.635 |
0.1021 |
|
| sewers_rain |
2.481 |
4.271 |
0.5808 |
0.5614 |
|
| sewers_waste |
-6.139 |
5.702 |
-1.077 |
0.2816 |
|
| wharves_city |
6.9 |
5.089 |
1.356 |
0.1751 |
|
| wharves_industry |
-17.11 |
7.422 |
-2.305 |
0.02117 |
* |
| fisheries_trap |
0.2165 |
0.2923 |
0.7407 |
0.4589 |
|
| fisheries_trawl |
-2.896 |
1.633 |
-1.774 |
0.07609 |
|
| fisheries_net |
-1.098 |
240.2 |
-0.00457 |
0.9964 |
|
| fisheries_dredge |
3.109 |
1.821 |
1.708 |
0.08769 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
16.3 |
12.9 |
9.34 |
12.7 |
6.1 |
11 |
4.71 |
11.3 |
16.9 |
16.1 |
20.2 |
1.25 |
2.88 |
1 |
3.04 |

Akanthophoreus gracilis
## SDM for: akanthophoreus_gracilis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.22
## Tjur's pseudo-R2 is: 0.22
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.771 |
0.8599 |
-2.059 |
0.03946 |
* |
| om |
0.7197 |
0.6251 |
1.151 |
0.2496 |
|
| gravel |
-0.2559 |
0.3398 |
-0.7532 |
0.4513 |
|
| silt |
-0.3166 |
0.5917 |
-0.535 |
0.5927 |
|
| clay |
-2.078 |
4.404 |
-0.4718 |
0.6371 |
|
| arsenic |
1.78 |
0.8504 |
2.093 |
0.03639 |
* |
| cadmium |
-1.195 |
0.6626 |
-1.804 |
0.07118 |
|
| copper |
-0.4515 |
0.8751 |
-0.5159 |
0.6059 |
|
| iron |
-1.121 |
1.031 |
-1.088 |
0.2766 |
|
| manganese |
-0.5799 |
0.9276 |
-0.6252 |
0.5319 |
|
| mercury |
0.669 |
0.5269 |
1.27 |
0.2041 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.13 |
1.22 |
2.16 |
1.12 |
2.32 |
1.94 |
2.81 |
2.54 |
2.94 |
1.75 |

Influence indices
## McFadden's pseudo-R2 is: 0.4
## Tjur's pseudo-R2 is: 0.41
## Pearson's pseudo-R2 is: 0.4
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-49.01 |
9593 |
-0.005108 |
0.9959 |
|
| aquaculture |
0.8755 |
3.741 |
0.234 |
0.815 |
|
| city |
-1.661 |
11.6 |
-0.1432 |
0.8861 |
|
| dredging_collect |
1.108 |
2.764 |
0.401 |
0.6884 |
|
| dredging_dump |
-3.884 |
3.925 |
-0.9894 |
0.3224 |
|
| industry |
3.212 |
1.799 |
1.786 |
0.07414 |
|
| shipping_mooring |
0.4912 |
4.994 |
0.09835 |
0.9217 |
|
| shipping_traffic |
2.458 |
1.558 |
1.578 |
0.1146 |
|
| sewers_rain |
-5.053 |
4.636 |
-1.09 |
0.2758 |
|
| sewers_waste |
5.851 |
6.716 |
0.8712 |
0.3836 |
|
| wharves_city |
2.392 |
9.793 |
0.2442 |
0.8071 |
|
| wharves_industry |
-2.126 |
4.916 |
-0.4325 |
0.6654 |
|
| fisheries_trap |
-0.02259 |
0.5791 |
-0.03901 |
0.9689 |
|
| fisheries_trawl |
0.0143 |
0.3483 |
0.04105 |
0.9673 |
|
| fisheries_net |
-484.5 |
99470 |
-0.004871 |
0.9961 |
|
| fisheries_dredge |
1.677 |
1.131 |
1.483 |
0.1381 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
12.4 |
24 |
8.31 |
11.3 |
4.32 |
17.4 |
4.31 |
14.3 |
23.1 |
20.6 |
14.1 |
1.14 |
1.52 |
1 |
2.06 |

Ameritella agilis
## SDM for: ameritella_agilis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.28
## Tjur's pseudo-R2 is: 0.2
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-4.757 |
1.603 |
-2.968 |
0.002999 |
* * |
| om |
-0.2253 |
0.9006 |
-0.2502 |
0.8024 |
|
| gravel |
0.3814 |
0.3982 |
0.9576 |
0.3383 |
|
| silt |
0.4714 |
0.7113 |
0.6627 |
0.5075 |
|
| clay |
-0.0246 |
1.79 |
-0.01375 |
0.989 |
|
| arsenic |
1.118 |
1.092 |
1.024 |
0.3059 |
|
| cadmium |
1.559 |
0.8039 |
1.94 |
0.05242 |
|
| copper |
-0.5074 |
0.9039 |
-0.5613 |
0.5746 |
|
| iron |
0.6741 |
0.7714 |
0.8739 |
0.3822 |
|
| manganese |
-5.377 |
2.602 |
-2.067 |
0.03877 |
* |
| mercury |
-0.7168 |
1.106 |
-0.6481 |
0.517 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.86 |
1.22 |
1.68 |
1.05 |
1.99 |
2.09 |
2.38 |
1.77 |
2.47 |
1.49 |

Influence indices
## McFadden's pseudo-R2 is: 0.67
## Tjur's pseudo-R2 is: 0.66
## Pearson's pseudo-R2 is: 0.69
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-11.97 |
37.02 |
-0.3232 |
0.7465 |
|
| aquaculture |
-4.204 |
22.96 |
-0.1831 |
0.8547 |
|
| city |
-18.85 |
20.01 |
-0.9422 |
0.3461 |
|
| dredging_collect |
4.373 |
23.92 |
0.1828 |
0.855 |
|
| dredging_dump |
26.8 |
27.7 |
0.9675 |
0.3333 |
|
| industry |
-4.854 |
14.32 |
-0.339 |
0.7346 |
|
| shipping_mooring |
1.527 |
9.388 |
0.1626 |
0.8708 |
|
| shipping_traffic |
-9.804 |
5.459 |
-1.796 |
0.07252 |
|
| sewers_rain |
20.45 |
38.76 |
0.5276 |
0.5978 |
|
| sewers_waste |
-17.64 |
63.79 |
-0.2765 |
0.7821 |
|
| wharves_city |
7.362 |
25.91 |
0.2841 |
0.7763 |
|
| wharves_industry |
-28.11 |
34.59 |
-0.8127 |
0.4164 |
|
| fisheries_trap |
0.9609 |
0.7229 |
1.329 |
0.1838 |
|
| fisheries_trawl |
0.9924 |
1.225 |
0.81 |
0.4179 |
|
| fisheries_net |
-1.18 |
378.9 |
-0.003114 |
0.9975 |
|
| fisheries_dredge |
1.981 |
4.632 |
0.4276 |
0.669 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
40.6 |
37.4 |
22.1 |
26 |
22.6 |
13.4 |
6.67 |
58.7 |
102 |
37.3 |
28.7 |
1.39 |
2.42 |
1 |
4.81 |

Ameroculodes edwardsi
## SDM for: ameroculodes_edwardsi
Abiotic parameters
## McFadden's pseudo-R2 is: 0.39
## Tjur's pseudo-R2 is: 0.28
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-314.2 |
81984 |
-0.003832 |
0.9969 |
|
| om |
1.651 |
1.331 |
1.24 |
0.2148 |
|
| gravel |
0.3069 |
0.4578 |
0.6704 |
0.5026 |
|
| silt |
-0.3607 |
1.104 |
-0.3267 |
0.7439 |
|
| clay |
-1687 |
447375 |
-0.003771 |
0.997 |
|
| arsenic |
0.6439 |
2.669 |
0.2413 |
0.8093 |
|
| cadmium |
-0.7745 |
1.33 |
-0.5821 |
0.5605 |
|
| copper |
0.7823 |
1.34 |
0.5837 |
0.5594 |
|
| iron |
-1.102 |
2.237 |
-0.4924 |
0.6224 |
|
| manganese |
-4.297 |
3.914 |
-1.098 |
0.2722 |
|
| mercury |
-0.3737 |
1.538 |
-0.243 |
0.808 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.24 |
1.31 |
2.14 |
1 |
1.99 |
2.14 |
2.2 |
2.8 |
2.27 |
1.71 |

Influence indices
## McFadden's pseudo-R2 is: -10.88
## Tjur's pseudo-R2 is: 0.38
## Pearson's pseudo-R2 is: 0.14
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.128e+15 |
7496963 |
-283847923 |
0 |
* * * |
| aquaculture |
1.508e+15 |
66771996 |
22587849 |
0 |
* * * |
| city |
4.043e+15 |
60515998 |
66814389 |
0 |
* * * |
| dredging_collect |
1.335e+15 |
47886179 |
27883094 |
0 |
* * * |
| dredging_dump |
-2.001e+15 |
55589796 |
-3.6e+07 |
0 |
* * * |
| industry |
2.308e+15 |
30193191 |
76456513 |
0 |
* * * |
| shipping_mooring |
2.114e+14 |
51224704 |
4126278 |
0 |
* * * |
| shipping_traffic |
1.552e+15 |
22815885 |
68021076 |
0 |
* * * |
| sewers_rain |
-2.312e+15 |
67164046 |
-34421159 |
0 |
* * * |
| sewers_waste |
2.884e+15 |
90359677 |
31919241 |
0 |
* * * |
| wharves_city |
-2.771e+15 |
72465526 |
-38238294 |
0 |
* * * |
| wharves_industry |
-2.112e+15 |
79067645 |
-26705228 |
0 |
* * * |
| fisheries_trap |
-6.957e+14 |
7277539 |
-95590201 |
0 |
* * * |
| fisheries_trawl |
2.102e+13 |
8821984 |
2382627 |
0 |
* * * |
| fisheries_net |
-7.662e+13 |
7219163 |
-10612931 |
0 |
* * * |
| fisheries_dredge |
1.903e+14 |
19454028 |
9784359 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Ampelisca vadorum
## SDM for: ampelisca_vadorum
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-138.7 |
383647 |
-0.0003616 |
0.9997 |
|
| om |
23.82 |
743810 |
3.202e-05 |
1 |
|
| gravel |
16.8 |
155156 |
0.0001083 |
0.9999 |
|
| silt |
-5.391 |
440444 |
-1.224e-05 |
1 |
|
| clay |
-85.72 |
2750414 |
-3.117e-05 |
1 |
|
| arsenic |
28.19 |
392691 |
7.179e-05 |
0.9999 |
|
| cadmium |
-50.24 |
2550096 |
-1.97e-05 |
1 |
|
| copper |
-10.97 |
1315122 |
-8.341e-06 |
1 |
|
| iron |
-9.734 |
111701 |
-8.715e-05 |
0.9999 |
|
| manganese |
24.98 |
2908626 |
8.587e-06 |
1 |
|
| mercury |
-36.25 |
3440016 |
-1.054e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.2 |
6.67 |
8.91 |
4.86 |
6.33 |
27.5 |
15.2 |
1.88 |
24.2 |
33.1 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-45.89 |
81532 |
-0.0005628 |
0.9996 |
|
| aquaculture |
17.39 |
823645 |
2.111e-05 |
1 |
|
| city |
-8.247 |
649404 |
-1.27e-05 |
1 |
|
| dredging_collect |
4.963 |
615293 |
8.066e-06 |
1 |
|
| dredging_dump |
1.678 |
624796 |
2.686e-06 |
1 |
|
| industry |
-18.92 |
414071 |
-4.569e-05 |
1 |
|
| shipping_mooring |
-8.321 |
665104 |
-1.251e-05 |
1 |
|
| shipping_traffic |
17.95 |
220308 |
8.15e-05 |
0.9999 |
|
| sewers_rain |
-56 |
976368 |
-5.736e-05 |
1 |
|
| sewers_waste |
66.1 |
1193278 |
5.54e-05 |
1 |
|
| wharves_city |
9.151 |
874298 |
1.047e-05 |
1 |
|
| wharves_industry |
-6.093 |
588168 |
-1.036e-05 |
1 |
|
| fisheries_trap |
-36.56 |
400329 |
-9.133e-05 |
0.9999 |
|
| fisheries_trawl |
1.048 |
71933 |
1.458e-05 |
1 |
|
| fisheries_net |
3.805 |
96171 |
3.956e-05 |
1 |
|
| fisheries_dredge |
-2.15 |
180119 |
-1.194e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
16.4 |
20.7 |
20.4 |
21.5 |
11.5 |
17.7 |
4.35 |
15.2 |
22.8 |
28.1 |
18.4 |
3.25 |
3.22 |
1.7 |
2.27 |

Amphipoda
## SDM for: amphipoda
Abiotic parameters
## McFadden's pseudo-R2 is: 0.06
## Tjur's pseudo-R2 is: 0.07
## Pearson's pseudo-R2 is: 0.07
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-0.6341 |
0.2661 |
-2.383 |
0.01718 |
* |
| om |
0.2529 |
0.4543 |
0.5566 |
0.5778 |
|
| gravel |
0.141 |
0.2795 |
0.5047 |
0.6138 |
|
| silt |
-0.07723 |
0.4908 |
-0.1574 |
0.875 |
|
| clay |
0.2773 |
0.668 |
0.4152 |
0.678 |
|
| arsenic |
0.4102 |
0.4092 |
1.002 |
0.3162 |
|
| cadmium |
0.1656 |
0.3405 |
0.4864 |
0.6267 |
|
| copper |
-0.1501 |
0.5391 |
-0.2784 |
0.7807 |
|
| iron |
-0.5428 |
0.7224 |
-0.7515 |
0.4524 |
|
| manganese |
0.176 |
0.4838 |
0.3639 |
0.716 |
|
| mercury |
0.2019 |
0.3908 |
0.5165 |
0.6055 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.98 |
1.31 |
2.14 |
1.1 |
1.61 |
1.4 |
2.35 |
2.34 |
1.99 |
1.55 |

Influence indices
## McFadden's pseudo-R2 is: 0.24
## Tjur's pseudo-R2 is: 0.28
## Pearson's pseudo-R2 is: 0.28
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-47.03 |
6419 |
-0.007327 |
0.9942 |
|
| aquaculture |
-4.229 |
2.448 |
-1.728 |
0.08405 |
|
| city |
2.732 |
2.985 |
0.9153 |
0.3601 |
|
| dredging_collect |
0.3684 |
1.909 |
0.193 |
0.847 |
|
| dredging_dump |
5.328 |
2.684 |
1.985 |
0.04715 |
* |
| industry |
3.257 |
1.217 |
2.675 |
0.007467 |
* * |
| shipping_mooring |
-2.543 |
2.066 |
-1.231 |
0.2183 |
|
| shipping_traffic |
-0.2417 |
0.992 |
-0.2437 |
0.8075 |
|
| sewers_rain |
3.178 |
3.076 |
1.033 |
0.3016 |
|
| sewers_waste |
-3.541 |
3.899 |
-0.9083 |
0.3637 |
|
| wharves_city |
-2.933 |
3.271 |
-0.8967 |
0.3699 |
|
| wharves_industry |
-7.405 |
4.007 |
-1.848 |
0.06461 |
|
| fisheries_trap |
0.4169 |
0.254 |
1.641 |
0.1007 |
|
| fisheries_trawl |
0.1173 |
0.2941 |
0.3989 |
0.69 |
|
| fisheries_net |
-478.1 |
66555 |
-0.007183 |
0.9943 |
|
| fisheries_dredge |
0.3911 |
0.694 |
0.5636 |
0.573 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.2 |
10.9 |
6.61 |
9.28 |
4.74 |
7.87 |
3.63 |
11.4 |
15.5 |
11.1 |
13.9 |
1.22 |
1.38 |
1 |
1.76 |

Anonyx lilljeborgi
## SDM for: anonyx_lilljeborgi
Abiotic parameters
## McFadden's pseudo-R2 is: 0.12
## Tjur's pseudo-R2 is: 0.1
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.589 |
0.581 |
-4.455 |
8.373e-06 |
* * * |
| om |
0.06397 |
0.7658 |
0.08353 |
0.9334 |
|
| gravel |
-0.2129 |
0.4163 |
-0.5113 |
0.6091 |
|
| silt |
-0.3808 |
0.6665 |
-0.5714 |
0.5677 |
|
| clay |
-0.05949 |
1.455 |
-0.04089 |
0.9674 |
|
| arsenic |
-0.5382 |
1.396 |
-0.3854 |
0.7 |
|
| cadmium |
-0.4654 |
0.5505 |
-0.8455 |
0.3978 |
|
| copper |
-0.3638 |
0.9078 |
-0.4007 |
0.6886 |
|
| iron |
-0.9676 |
1.131 |
-0.8553 |
0.3924 |
|
| manganese |
1.03 |
1.088 |
0.9469 |
0.3437 |
|
| mercury |
0.005688 |
0.7112 |
0.007998 |
0.9936 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.67 |
1.2 |
1.76 |
1.07 |
1.84 |
1.36 |
2.04 |
2.1 |
2.48 |
1.6 |

Influence indices
## McFadden's pseudo-R2 is: 0.36
## Tjur's pseudo-R2 is: 0.32
## Pearson's pseudo-R2 is: 0.33
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-47.62 |
9701 |
-0.004908 |
0.9961 |
|
| aquaculture |
-6.816 |
7.895 |
-0.8633 |
0.388 |
|
| city |
-16.81 |
16.99 |
-0.9892 |
0.3226 |
|
| dredging_collect |
-4.376 |
4.464 |
-0.9801 |
0.327 |
|
| dredging_dump |
3.381 |
4.36 |
0.7753 |
0.4381 |
|
| industry |
0.728 |
2.93 |
0.2485 |
0.8038 |
|
| shipping_mooring |
-3.534 |
5.65 |
-0.6254 |
0.5317 |
|
| shipping_traffic |
2.857 |
2.531 |
1.129 |
0.2589 |
|
| sewers_rain |
0.7968 |
4.61 |
0.1728 |
0.8628 |
|
| sewers_waste |
-5.441 |
8.423 |
-0.646 |
0.5183 |
|
| wharves_city |
17.11 |
17.04 |
1.004 |
0.3152 |
|
| wharves_industry |
-3.693 |
6.282 |
-0.5879 |
0.5566 |
|
| fisheries_trap |
-0.1464 |
0.5325 |
-0.275 |
0.7833 |
|
| fisheries_trawl |
-9.673 |
8.778 |
-1.102 |
0.2705 |
|
| fisheries_net |
-436.7 |
100591 |
-0.004341 |
0.9965 |
|
| fisheries_dredge |
-1.991 |
1.905 |
-1.045 |
0.2959 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
20.6 |
38.6 |
8.51 |
9.15 |
4.25 |
14.3 |
5.88 |
10.6 |
18.8 |
41.7 |
12 |
1.13 |
1.6 |
1 |
2.38 |

Anthozoa
## SDM for: anthozoa
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Arcteobia anticostiensis
## SDM for: arcteobia_anticostiensis
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-157.4 |
448856 |
-0.0003506 |
0.9997 |
|
| om |
-9.446 |
244648 |
-3.861e-05 |
1 |
|
| gravel |
1.884 |
549794 |
3.427e-06 |
1 |
|
| silt |
25.73 |
120585 |
0.0002134 |
0.9998 |
|
| clay |
3.612 |
2041819 |
1.769e-06 |
1 |
|
| arsenic |
-99.38 |
527677 |
-0.0001883 |
0.9998 |
|
| cadmium |
46.64 |
389449 |
0.0001198 |
0.9999 |
|
| copper |
7.77 |
320737 |
2.423e-05 |
1 |
|
| iron |
5.156 |
458233 |
1.125e-05 |
1 |
|
| manganese |
-96.32 |
760707 |
-0.0001266 |
0.9999 |
|
| mercury |
-33.49 |
301617 |
-0.000111 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.49 |
2.61 |
2.07 |
1.42 |
1.71 |
5.8 |
3.83 |
4.91 |
6.15 |
2.97 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-132 |
261580 |
-0.0005048 |
0.9996 |
|
| aquaculture |
29.9 |
2012429 |
1.486e-05 |
1 |
|
| city |
-44.81 |
2524621 |
-1.775e-05 |
1 |
|
| dredging_collect |
-159.7 |
1088442 |
-0.0001467 |
0.9999 |
|
| dredging_dump |
-24.51 |
2547376 |
-9.621e-06 |
1 |
|
| industry |
-32.52 |
1014058 |
-3.207e-05 |
1 |
|
| shipping_mooring |
-1.868 |
1479903 |
-1.262e-06 |
1 |
|
| shipping_traffic |
-16.82 |
413635 |
-4.066e-05 |
1 |
|
| sewers_rain |
-9.975 |
1818813 |
-5.484e-06 |
1 |
|
| sewers_waste |
47.74 |
3083842 |
1.548e-05 |
1 |
|
| wharves_city |
64.8 |
3296940 |
1.965e-05 |
1 |
|
| wharves_industry |
194.3 |
1348004 |
0.0001441 |
0.9999 |
|
| fisheries_trap |
-166.4 |
673787 |
-0.0002469 |
0.9998 |
|
| fisheries_trawl |
-108.3 |
748721 |
-0.0001446 |
0.9999 |
|
| fisheries_net |
5.202 |
183416 |
2.836e-05 |
1 |
|
| fisheries_dredge |
51.23 |
177084 |
0.0002893 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
30.6 |
29.4 |
14.3 |
34.2 |
9.02 |
22.4 |
6.04 |
31.7 |
53.2 |
36.3 |
18.3 |
2.25 |
2.44 |
1.2 |
2.14 |

Arrhoges occidentalis
## SDM for: arrhoges_occidentalis
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-697.8 |
1241825 |
-0.0005619 |
0.9996 |
|
| om |
-307.8 |
642564 |
-0.0004791 |
0.9996 |
|
| gravel |
-46.14 |
509443 |
-9.057e-05 |
0.9999 |
|
| silt |
215.6 |
523288 |
0.000412 |
0.9997 |
|
| clay |
-835.8 |
3182082 |
-0.0002626 |
0.9998 |
|
| arsenic |
-55.85 |
994346 |
-5.617e-05 |
1 |
|
| cadmium |
-13.95 |
268185 |
-5.203e-05 |
1 |
|
| copper |
63.83 |
458454 |
0.0001392 |
0.9999 |
|
| iron |
-218.9 |
718355 |
-0.0003047 |
0.9998 |
|
| manganese |
130 |
862172 |
0.0001508 |
0.9999 |
|
| mercury |
-343.6 |
763174 |
-0.0004503 |
0.9996 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.49 |
1.68 |
10.5 |
2.47 |
5.16 |
5.64 |
4.88 |
4.87 |
5.22 |
7.25 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-60.14 |
143179 |
-0.00042 |
0.9997 |
|
| aquaculture |
107.6 |
1441852 |
7.459e-05 |
0.9999 |
|
| city |
-1.428 |
1895135 |
-7.536e-07 |
1 |
|
| dredging_collect |
67.81 |
1364124 |
4.971e-05 |
1 |
|
| dredging_dump |
40.36 |
2054967 |
1.964e-05 |
1 |
|
| industry |
-18.71 |
679982 |
-2.752e-05 |
1 |
|
| shipping_mooring |
25.27 |
1492206 |
1.693e-05 |
1 |
|
| shipping_traffic |
14.55 |
518221 |
2.807e-05 |
1 |
|
| sewers_rain |
-152.9 |
2079740 |
-7.351e-05 |
0.9999 |
|
| sewers_waste |
219.1 |
2685278 |
8.161e-05 |
0.9999 |
|
| wharves_city |
-6.408 |
2320513 |
-2.761e-06 |
1 |
|
| wharves_industry |
-124.7 |
3709266 |
-3.361e-05 |
1 |
|
| fisheries_trap |
1.818 |
81054 |
2.243e-05 |
1 |
|
| fisheries_trawl |
6.144 |
131410 |
4.676e-05 |
1 |
|
| fisheries_net |
11.53 |
144282 |
7.992e-05 |
0.9999 |
|
| fisheries_dredge |
-18.85 |
344009 |
-5.479e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
26.5 |
26.7 |
22.6 |
34.8 |
8.97 |
28.8 |
9.9 |
41.8 |
55.2 |
32.6 |
59.7 |
1.53 |
1.91 |
1.55 |
1.89 |

Astarte sp
## SDM for: astarte_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.49
## Tjur's pseudo-R2 is: 0.35
## Pearson's pseudo-R2 is: 0.32
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-363.5 |
106846 |
-0.003402 |
0.9973 |
|
| om |
-1.474 |
2.398 |
-0.6148 |
0.5387 |
|
| gravel |
-1.021 |
1.167 |
-0.8752 |
0.3814 |
|
| silt |
-1.461 |
1.724 |
-0.8473 |
0.3968 |
|
| clay |
-1941 |
583040 |
-0.003328 |
0.9973 |
|
| arsenic |
2.285 |
1.967 |
1.162 |
0.2453 |
|
| cadmium |
-2.472 |
1.975 |
-1.252 |
0.2106 |
|
| copper |
3.352 |
2.021 |
1.659 |
0.09713 |
|
| iron |
-0.1492 |
1.825 |
-0.08178 |
0.9348 |
|
| manganese |
-7.684 |
6 |
-1.281 |
0.2003 |
|
| mercury |
0.8402 |
2.708 |
0.3103 |
0.7563 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.8 |
1.2 |
1.84 |
1 |
1.95 |
2.1 |
2.22 |
2.03 |
3.05 |
2.21 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-334.9 |
961323 |
-0.0003483 |
0.9997 |
|
| aquaculture |
-773.8 |
6568153 |
-0.0001178 |
0.9999 |
|
| city |
1307 |
6289563 |
0.0002078 |
0.9998 |
|
| dredging_collect |
817.9 |
4639745 |
0.0001763 |
0.9999 |
|
| dredging_dump |
437 |
6511840 |
6.71e-05 |
0.9999 |
|
| industry |
322.4 |
1962421 |
0.0001643 |
0.9999 |
|
| shipping_mooring |
19.7 |
7149191 |
2.756e-06 |
1 |
|
| shipping_traffic |
400.1 |
4353995 |
9.189e-05 |
0.9999 |
|
| sewers_rain |
848.7 |
6023976 |
0.0001409 |
0.9999 |
|
| sewers_waste |
-1559 |
7554110 |
-0.0002064 |
0.9998 |
|
| wharves_city |
-1404 |
6946723 |
-0.0002022 |
0.9998 |
|
| wharves_industry |
-1399 |
4168846 |
-0.0003356 |
0.9997 |
|
| fisheries_trap |
-80.57 |
851379 |
-9.463e-05 |
0.9999 |
|
| fisheries_trawl |
3.774 |
2717836 |
1.389e-06 |
1 |
|
| fisheries_net |
25.73 |
6458767 |
3.984e-06 |
1 |
|
| fisheries_dredge |
79.87 |
751952 |
0.0001062 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
66.6 |
79.1 |
54.5 |
93.7 |
26.5 |
98.1 |
79 |
107 |
99.9 |
89.9 |
60.1 |
3.98 |
9.78 |
1 |
16.9 |

Axinopsida orbiculata
## SDM for: axinopsida_orbiculata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.15
## Tjur's pseudo-R2 is: 0.13
## Pearson's pseudo-R2 is: 0.15
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-350.2 |
50091 |
-0.006991 |
0.9944 |
|
| om |
0.179 |
0.7083 |
0.2527 |
0.8005 |
|
| gravel |
0.1448 |
0.3706 |
0.3909 |
0.6959 |
|
| silt |
0.4586 |
0.7545 |
0.6079 |
0.5433 |
|
| clay |
-1899 |
273336 |
-0.006949 |
0.9945 |
|
| arsenic |
0.1748 |
0.4868 |
0.3591 |
0.7195 |
|
| cadmium |
0.4905 |
0.4235 |
1.158 |
0.2467 |
|
| copper |
-0.04828 |
0.5662 |
-0.08528 |
0.932 |
|
| iron |
0.02784 |
0.3768 |
0.0739 |
0.9411 |
|
| manganese |
-0.5204 |
0.802 |
-0.6489 |
0.5164 |
|
| mercury |
-1.75 |
0.8706 |
-2.01 |
0.04448 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.03 |
1.23 |
2.22 |
1 |
1.46 |
1.49 |
1.73 |
1.4 |
1.67 |
1.65 |

Influence indices
## McFadden's pseudo-R2 is: 0.39
## Tjur's pseudo-R2 is: 0.39
## Pearson's pseudo-R2 is: 0.4
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-4.35 |
22.24 |
-0.1956 |
0.845 |
|
| aquaculture |
-9.769 |
5.583 |
-1.75 |
0.08018 |
|
| city |
2.463 |
6.809 |
0.3618 |
0.7175 |
|
| dredging_collect |
-8.538 |
4.209 |
-2.028 |
0.04252 |
* |
| dredging_dump |
5.18 |
4.323 |
1.198 |
0.2308 |
|
| industry |
4.782 |
2.272 |
2.105 |
0.03532 |
* |
| shipping_mooring |
-11.07 |
5.824 |
-1.9 |
0.05737 |
|
| shipping_traffic |
-0.1789 |
1.745 |
-0.1025 |
0.9183 |
|
| sewers_rain |
-8.51 |
5.806 |
-1.466 |
0.1427 |
|
| sewers_waste |
5.623 |
7.137 |
0.7878 |
0.4308 |
|
| wharves_city |
3.271 |
8.576 |
0.3814 |
0.7029 |
|
| wharves_industry |
2.262 |
6.356 |
0.356 |
0.7219 |
|
| fisheries_trap |
0.2228 |
0.4098 |
0.5437 |
0.5866 |
|
| fisheries_trawl |
1.244 |
0.6119 |
2.033 |
0.0421 |
* |
| fisheries_net |
0.1132 |
230.4 |
0.0004914 |
0.9996 |
|
| fisheries_dredge |
-0.6707 |
1.376 |
-0.4873 |
0.6261 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
16.1 |
18.7 |
11.9 |
12 |
6.45 |
14.9 |
4.58 |
11.8 |
16.8 |
24.4 |
17.3 |
1.78 |
1.77 |
1 |
1.99 |

Axiothella catenata
## SDM for: axiothella_catenata
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-124.5 |
239182 |
-0.0005206 |
0.9996 |
|
| om |
48.35 |
223221 |
0.0002166 |
0.9998 |
|
| gravel |
4.668 |
104016 |
4.488e-05 |
1 |
|
| silt |
15.06 |
299118 |
5.035e-05 |
1 |
|
| clay |
29.53 |
414785 |
7.12e-05 |
0.9999 |
|
| arsenic |
-98.17 |
2115404 |
-4.641e-05 |
1 |
|
| cadmium |
6.723 |
548780 |
1.225e-05 |
1 |
|
| copper |
30.86 |
658438 |
4.687e-05 |
1 |
|
| iron |
-96.12 |
1648993 |
-5.829e-05 |
1 |
|
| manganese |
19.64 |
974923 |
2.015e-05 |
1 |
|
| mercury |
13.78 |
199905 |
6.895e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.35 |
1.16 |
3.13 |
1.12 |
11.8 |
5.99 |
5.84 |
14.1 |
7.66 |
3.88 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-172.6 |
710906 |
-0.0002428 |
0.9998 |
|
| aquaculture |
274.2 |
12346720 |
2.221e-05 |
1 |
|
| city |
438 |
10605133 |
4.13e-05 |
1 |
|
| dredging_collect |
-41.52 |
7330176 |
-5.665e-06 |
1 |
|
| dredging_dump |
402.9 |
9883995 |
4.077e-05 |
1 |
|
| industry |
-113 |
2977563 |
-3.795e-05 |
1 |
|
| shipping_mooring |
266.6 |
2888043 |
9.232e-05 |
0.9999 |
|
| shipping_traffic |
144 |
2229645 |
6.46e-05 |
0.9999 |
|
| sewers_rain |
-202.5 |
24235051 |
-8.357e-06 |
1 |
|
| sewers_waste |
127.5 |
31882221 |
3.999e-06 |
1 |
|
| wharves_city |
-519.7 |
9085034 |
-5.721e-05 |
1 |
|
| wharves_industry |
-335.1 |
7301761 |
-4.59e-05 |
1 |
|
| fisheries_trap |
2.331 |
165526 |
1.408e-05 |
1 |
|
| fisheries_trawl |
21.04 |
1544162 |
1.363e-05 |
1 |
|
| fisheries_net |
43.25 |
1455681 |
2.971e-05 |
1 |
|
| fisheries_dredge |
99.04 |
4693485 |
2.11e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
139 |
111 |
87.7 |
111 |
20 |
33.2 |
23.7 |
225 |
345 |
93.9 |
79.1 |
1.26 |
18.4 |
5.76 |
49.2 |

Bathymedon longimanus
## SDM for: bathymedon_longimanus
Abiotic parameters
## McFadden's pseudo-R2 is: 0.75
## Tjur's pseudo-R2 is: 0.68
## Pearson's pseudo-R2 is: 0.67
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-45.76 |
6751 |
-0.006779 |
0.9946 |
|
| om |
-0.9562 |
5.027 |
-0.1902 |
0.8491 |
|
| gravel |
-16.64 |
6173 |
-0.002695 |
0.9978 |
|
| silt |
3.376 |
5.74 |
0.5882 |
0.5564 |
|
| clay |
-66.12 |
35621 |
-0.001856 |
0.9985 |
|
| arsenic |
-19.88 |
22.7 |
-0.8757 |
0.3812 |
|
| cadmium |
7.025 |
5.106 |
1.376 |
0.1689 |
|
| copper |
2.347 |
4.458 |
0.5264 |
0.5986 |
|
| iron |
0.8915 |
9.592 |
0.09293 |
0.926 |
|
| manganese |
-18.37 |
18.36 |
-1.001 |
0.3169 |
|
| mercury |
-11.6 |
8.551 |
-1.356 |
0.175 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.63 |
1 |
5.49 |
1 |
3.42 |
3.38 |
2.35 |
4.45 |
3.85 |
2.99 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-150.9 |
279889 |
-0.0005392 |
0.9996 |
|
| aquaculture |
546.3 |
2309983 |
0.0002365 |
0.9998 |
|
| city |
252.9 |
2641627 |
9.573e-05 |
0.9999 |
|
| dredging_collect |
-76.37 |
1312369 |
-5.819e-05 |
1 |
|
| dredging_dump |
-39.15 |
1310039 |
-2.989e-05 |
1 |
|
| industry |
-145.9 |
1131859 |
-0.0001289 |
0.9999 |
|
| shipping_mooring |
182.8 |
2323752 |
7.866e-05 |
0.9999 |
|
| shipping_traffic |
20.47 |
555277 |
3.686e-05 |
1 |
|
| sewers_rain |
-580.1 |
2351575 |
-0.0002467 |
0.9998 |
|
| sewers_waste |
800.3 |
3192021 |
0.0002507 |
0.9998 |
|
| wharves_city |
-236.6 |
3057641 |
-7.739e-05 |
0.9999 |
|
| wharves_industry |
205.5 |
2118130 |
9.701e-05 |
0.9999 |
|
| fisheries_trap |
-15.54 |
108138 |
-0.0001437 |
0.9999 |
|
| fisheries_trawl |
1.348 |
838528 |
1.608e-06 |
1 |
|
| fisheries_net |
42.88 |
240506 |
0.0001783 |
0.9999 |
|
| fisheries_dredge |
52.99 |
170692 |
0.0003104 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
50.2 |
80.2 |
37.2 |
39 |
15.6 |
59.3 |
16.4 |
57.8 |
78.2 |
95.5 |
58 |
1.28 |
3.11 |
1.57 |
3.36 |

Bathymedon obtusifrons
## SDM for: bathymedon_obtusifrons
Abiotic parameters
## McFadden's pseudo-R2 is: -12.23
## Tjur's pseudo-R2 is: -0.01
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-3.301e+15 |
7978909 |
-413766906 |
0 |
* * * |
| om |
-1.493e+15 |
14257309 |
-104685824 |
0 |
* * * |
| gravel |
7.237e+14 |
8714549 |
83047230 |
0 |
* * * |
| silt |
1.384e+15 |
15274819 |
90612828 |
0 |
* * * |
| clay |
-7.527e+14 |
21858102 |
-34434528 |
0 |
* * * |
| arsenic |
-1.752e+15 |
11951982 |
-146562517 |
0 |
* * * |
| cadmium |
6.267e+14 |
10400421 |
60260861 |
0 |
* * * |
| copper |
1.939e+15 |
13687347 |
141670271 |
0 |
* * * |
| iron |
-4.869e+14 |
9620115 |
-50615564 |
0 |
* * * |
| manganese |
-5.417e+14 |
14288707 |
-37912203 |
0 |
* * * |
| mercury |
-6.123e+14 |
12068606 |
-50738739 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-108.2 |
230852 |
-0.0004686 |
0.9996 |
|
| aquaculture |
-372.7 |
7074900 |
-5.267e-05 |
1 |
|
| city |
-426.4 |
3308637 |
-0.0001289 |
0.9999 |
|
| dredging_collect |
-235.4 |
21696254 |
-1.085e-05 |
1 |
|
| dredging_dump |
-82.52 |
6321537 |
-1.305e-05 |
1 |
|
| industry |
213.3 |
1182221 |
0.0001805 |
0.9999 |
|
| shipping_mooring |
-280.9 |
11462006 |
-2.451e-05 |
1 |
|
| shipping_traffic |
-98.85 |
3244147 |
-3.047e-05 |
1 |
|
| sewers_rain |
103.2 |
6422833 |
1.607e-05 |
1 |
|
| sewers_waste |
-34.09 |
8749498 |
-3.896e-06 |
1 |
|
| wharves_city |
620.2 |
4779428 |
0.0001298 |
0.9999 |
|
| wharves_industry |
91.44 |
28604720 |
3.197e-06 |
1 |
|
| fisheries_trap |
-14.2 |
464038 |
-3.06e-05 |
1 |
|
| fisheries_trawl |
11.79 |
560768 |
2.102e-05 |
1 |
|
| fisheries_net |
5.979 |
272327 |
2.195e-05 |
1 |
|
| fisheries_dredge |
40.26 |
6081530 |
6.621e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
58.6 |
47.9 |
255 |
80.8 |
13 |
103 |
34.1 |
46.3 |
61.4 |
78.8 |
312 |
9.76 |
3.81 |
1.77 |
36.1 |

Bipalponephtys neotena
## SDM for: bipalponephtys_neotena
Abiotic parameters
## McFadden's pseudo-R2 is: 0.34
## Tjur's pseudo-R2 is: 0.35
## Pearson's pseudo-R2 is: 0.36
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
2.725 |
0.7255 |
3.757 |
0.0001721 |
* * * |
| om |
3.351 |
1.061 |
3.158 |
0.001587 |
* * |
| gravel |
0.1487 |
0.3701 |
0.4019 |
0.6878 |
|
| silt |
0.07557 |
0.5909 |
0.1279 |
0.8982 |
|
| clay |
0.1914 |
0.7378 |
0.2595 |
0.7953 |
|
| arsenic |
-0.5925 |
0.6365 |
-0.931 |
0.3519 |
|
| cadmium |
0.2332 |
0.5077 |
0.4593 |
0.646 |
|
| copper |
-0.6341 |
0.663 |
-0.9564 |
0.3388 |
|
| iron |
-0.4925 |
0.3589 |
-1.372 |
0.17 |
|
| manganese |
0.4055 |
0.9583 |
0.4231 |
0.6722 |
|
| mercury |
-0.187 |
0.8137 |
-0.2298 |
0.8182 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.62 |
1.21 |
1.63 |
1.13 |
1.56 |
1.62 |
1.82 |
1.47 |
1.89 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0.28
## Tjur's pseudo-R2 is: 0.28
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
75.64 |
8314 |
0.009098 |
0.9927 |
|
| aquaculture |
-2.509 |
3.254 |
-0.7713 |
0.4405 |
|
| city |
-1.976 |
2.874 |
-0.6875 |
0.4918 |
|
| dredging_collect |
0.6187 |
2.508 |
0.2466 |
0.8052 |
|
| dredging_dump |
2.541 |
2.718 |
0.9349 |
0.3498 |
|
| industry |
3.105 |
1.637 |
1.897 |
0.05787 |
|
| shipping_mooring |
-0.9498 |
2.381 |
-0.3989 |
0.69 |
|
| shipping_traffic |
0.1875 |
1.08 |
0.1737 |
0.8621 |
|
| sewers_rain |
0.01476 |
3.206 |
0.004605 |
0.9963 |
|
| sewers_waste |
0.9926 |
4.578 |
0.2168 |
0.8283 |
|
| wharves_city |
2.743 |
3.61 |
0.7599 |
0.4473 |
|
| wharves_industry |
-6.209 |
3.43 |
-1.81 |
0.07025 |
|
| fisheries_trap |
0.5818 |
0.7865 |
0.7397 |
0.4595 |
|
| fisheries_trawl |
-0.116 |
0.3149 |
-0.3683 |
0.7127 |
|
| fisheries_net |
763.1 |
86210 |
0.008851 |
0.9929 |
|
| fisheries_dredge |
1.454 |
1.483 |
0.98 |
0.3271 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.9 |
10.4 |
8.85 |
9.53 |
5.97 |
6.98 |
3.9 |
9.91 |
13.9 |
12.7 |
12.1 |
1.1 |
1.46 |
1 |
2.15 |

Boreochiton ruber
## SDM for: boreochiton_ruber
Abiotic parameters
## McFadden's pseudo-R2 is: 0.61
## Tjur's pseudo-R2 is: 0.51
## Pearson's pseudo-R2 is: 0.54
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-416.3 |
128343 |
-0.003244 |
0.9974 |
|
| om |
-14.19 |
12.19 |
-1.164 |
0.2446 |
|
| gravel |
0.61 |
1.068 |
0.5713 |
0.5678 |
|
| silt |
3.379 |
3.183 |
1.062 |
0.2883 |
|
| clay |
-2147 |
700348 |
-0.003065 |
0.9976 |
|
| arsenic |
-9.387 |
11.1 |
-0.8457 |
0.3977 |
|
| cadmium |
1.35 |
2.559 |
0.5273 |
0.598 |
|
| copper |
4.62 |
5.155 |
0.8962 |
0.3702 |
|
| iron |
-1.693 |
7.135 |
-0.2372 |
0.8125 |
|
| manganese |
-4.797 |
8.229 |
-0.5829 |
0.56 |
|
| mercury |
-5.783 |
5.647 |
-1.024 |
0.3058 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.44 |
2.23 |
3.53 |
1 |
2.04 |
1.69 |
3.61 |
3.89 |
2.79 |
3.31 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-419.6 |
1299699 |
-0.0003229 |
0.9997 |
|
| aquaculture |
-393.4 |
6744588 |
-5.832e-05 |
1 |
|
| city |
1195 |
3482669 |
0.0003432 |
0.9997 |
|
| dredging_collect |
156.6 |
3564214 |
4.394e-05 |
1 |
|
| dredging_dump |
444 |
2609046 |
0.0001702 |
0.9999 |
|
| industry |
-242 |
2960165 |
-8.175e-05 |
0.9999 |
|
| shipping_mooring |
68.55 |
4602557 |
1.489e-05 |
1 |
|
| shipping_traffic |
20.41 |
1266934 |
1.611e-05 |
1 |
|
| sewers_rain |
1158 |
3858791 |
3e-04 |
0.9998 |
|
| sewers_waste |
-2138 |
6717074 |
-0.0003183 |
0.9997 |
|
| wharves_city |
-1550 |
5275231 |
-0.0002938 |
0.9998 |
|
| wharves_industry |
41.58 |
3430100 |
1.212e-05 |
1 |
|
| fisheries_trap |
-10.43 |
99035 |
-0.0001053 |
0.9999 |
|
| fisheries_trawl |
14.27 |
95952 |
0.0001487 |
0.9999 |
|
| fisheries_net |
7.98 |
6464702 |
1.234e-06 |
1 |
|
| fisheries_dredge |
15.65 |
276619 |
5.658e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
226 |
90.7 |
66.4 |
51.5 |
111 |
75.2 |
35.8 |
107 |
203 |
74 |
71.4 |
1.36 |
2.65 |
1.07 |
5.19 |

Brachydiastylis sp
## SDM for: brachydiastylis_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.56
## Tjur's pseudo-R2 is: 0.43
## Pearson's pseudo-R2 is: 0.45
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-259.1 |
123682 |
-0.002095 |
0.9983 |
|
| om |
-4.988 |
5.363 |
-0.9301 |
0.3523 |
|
| gravel |
1.193 |
0.9731 |
1.226 |
0.2201 |
|
| silt |
3.894 |
4.309 |
0.9038 |
0.3661 |
|
| clay |
-1353 |
674913 |
-0.002005 |
0.9984 |
|
| arsenic |
1.927 |
3.475 |
0.5546 |
0.5792 |
|
| cadmium |
-1.356 |
2.631 |
-0.5151 |
0.6065 |
|
| copper |
-1.304 |
4.147 |
-0.3145 |
0.7532 |
|
| iron |
-0.2421 |
1.607 |
-0.1506 |
0.8803 |
|
| manganese |
2.174 |
5.36 |
0.4057 |
0.685 |
|
| mercury |
-6.953 |
7.487 |
-0.9286 |
0.3531 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.93 |
1.89 |
3.95 |
1 |
1.56 |
2.17 |
1.89 |
1.31 |
2.33 |
2.63 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-44.02 |
78423 |
-0.0005613 |
0.9996 |
|
| aquaculture |
-2.298 |
1078585 |
-2.131e-06 |
1 |
|
| city |
9.953 |
892781 |
1.115e-05 |
1 |
|
| dredging_collect |
15.66 |
627088 |
2.498e-05 |
1 |
|
| dredging_dump |
14.01 |
531114 |
2.637e-05 |
1 |
|
| industry |
-13.15 |
455368 |
-2.887e-05 |
1 |
|
| shipping_mooring |
-3.474 |
818722 |
-4.243e-06 |
1 |
|
| shipping_traffic |
25.57 |
225177 |
0.0001135 |
0.9999 |
|
| sewers_rain |
-29.05 |
1993066 |
-1.457e-05 |
1 |
|
| sewers_waste |
26.45 |
2582489 |
1.024e-05 |
1 |
|
| wharves_city |
-8.742 |
1186999 |
-7.365e-06 |
1 |
|
| wharves_industry |
-34.78 |
636776 |
-5.461e-05 |
1 |
|
| fisheries_trap |
-35.95 |
481385 |
-7.467e-05 |
0.9999 |
|
| fisheries_trawl |
2.486 |
67342 |
3.692e-05 |
1 |
|
| fisheries_net |
3.375 |
138615 |
2.435e-05 |
1 |
|
| fisheries_dredge |
17.78 |
214484 |
8.29e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
21.9 |
27.4 |
21.2 |
17.9 |
13 |
21.5 |
4.61 |
34 |
50.8 |
36.2 |
19.3 |
4.08 |
3.31 |
2.45 |
4.02 |

Byblis gaimardii
## SDM for: byblis_gaimardii
Abiotic parameters
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.16
## Pearson's pseudo-R2 is: 0.17
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-314.3 |
80459 |
-0.003907 |
0.9969 |
|
| om |
0.06122 |
1.704 |
0.03594 |
0.9713 |
|
| gravel |
0.3874 |
0.4455 |
0.8696 |
0.3845 |
|
| silt |
-0.1092 |
1.346 |
-0.08116 |
0.9353 |
|
| clay |
-1687 |
439052 |
-0.003843 |
0.9969 |
|
| arsenic |
0.9056 |
2.505 |
0.3616 |
0.7177 |
|
| cadmium |
-0.6533 |
1.66 |
-0.3935 |
0.694 |
|
| copper |
-1.419 |
2.128 |
-0.6668 |
0.5049 |
|
| iron |
-0.5601 |
1.729 |
-0.324 |
0.746 |
|
| manganese |
-0.4225 |
4.268 |
-0.09899 |
0.9212 |
|
| mercury |
-0.4148 |
2.158 |
-0.1922 |
0.8476 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.61 |
1.26 |
2.06 |
1 |
1.47 |
1.59 |
1.47 |
1.47 |
1.89 |
1.79 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-133.5 |
633415 |
-0.0002108 |
0.9998 |
|
| aquaculture |
-33.81 |
3011137 |
-1.123e-05 |
1 |
|
| city |
-55.38 |
5123744 |
-1.081e-05 |
1 |
|
| dredging_collect |
-42.89 |
6933315 |
-6.186e-06 |
1 |
|
| dredging_dump |
-8.803 |
3069272 |
-2.868e-06 |
1 |
|
| industry |
-27.66 |
2327512 |
-1.188e-05 |
1 |
|
| shipping_mooring |
-60.48 |
3446335 |
-1.755e-05 |
1 |
|
| shipping_traffic |
65.97 |
849523 |
7.765e-05 |
0.9999 |
|
| sewers_rain |
-10.59 |
2937710 |
-3.604e-06 |
1 |
|
| sewers_waste |
40.72 |
6562802 |
6.205e-06 |
1 |
|
| wharves_city |
75.86 |
7142573 |
1.062e-05 |
1 |
|
| wharves_industry |
4.933 |
9122226 |
5.408e-07 |
1 |
|
| fisheries_trap |
-284.1 |
899705 |
-0.0003158 |
0.9997 |
|
| fisheries_trawl |
-1.333 |
136060 |
-9.8e-06 |
1 |
|
| fisheries_net |
4.65 |
260389 |
1.786e-05 |
1 |
|
| fisheries_dredge |
60.26 |
974431 |
6.184e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
34.2 |
45.8 |
84.9 |
36.4 |
17.1 |
42.4 |
10.3 |
41.3 |
89.8 |
63.7 |
110 |
1.49 |
3.11 |
1.04 |
8.79 |

Cancer irroratus
## SDM for: cancer_irroratus
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-511.8 |
862740 |
-0.0005932 |
0.9995 |
|
| om |
-261.7 |
454872 |
-0.0005754 |
0.9995 |
|
| gravel |
86.71 |
157831 |
0.0005494 |
0.9996 |
|
| silt |
75.79 |
203871 |
0.0003718 |
0.9997 |
|
| clay |
43.83 |
271758 |
0.0001613 |
0.9999 |
|
| arsenic |
54.42 |
2718342 |
2.002e-05 |
1 |
|
| cadmium |
229.9 |
761955 |
0.0003017 |
0.9998 |
|
| copper |
104.5 |
342807 |
0.0003048 |
0.9998 |
|
| iron |
-256.6 |
4070557 |
-6.304e-05 |
0.9999 |
|
| manganese |
-191.8 |
3350724 |
-5.723e-05 |
1 |
|
| mercury |
-14.6 |
1024508 |
-1.425e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.79 |
3.93 |
3.6 |
1.1 |
13.2 |
11 |
4.14 |
31.3 |
26.9 |
6.64 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-239.4 |
988003 |
-0.0002423 |
0.9998 |
|
| aquaculture |
1113 |
17795634 |
6.255e-05 |
1 |
|
| city |
385.5 |
2236045 |
0.0001724 |
0.9999 |
|
| dredging_collect |
133.7 |
12374406 |
1.08e-05 |
1 |
|
| dredging_dump |
-81.54 |
3307523 |
-2.465e-05 |
1 |
|
| industry |
-464.9 |
2014431 |
-0.0002308 |
0.9998 |
|
| shipping_mooring |
701.1 |
3892682 |
0.0001801 |
0.9999 |
|
| shipping_traffic |
186.5 |
2400153 |
7.772e-05 |
0.9999 |
|
| sewers_rain |
-409.1 |
16316413 |
-2.507e-05 |
1 |
|
| sewers_waste |
636.8 |
21629851 |
2.944e-05 |
1 |
|
| wharves_city |
-596.9 |
3843564 |
-0.0001553 |
0.9999 |
|
| wharves_industry |
15.41 |
12632497 |
1.22e-06 |
1 |
|
| fisheries_trap |
32.12 |
262169 |
0.0001225 |
0.9999 |
|
| fisheries_trawl |
-42.42 |
1651366 |
-2.569e-05 |
1 |
|
| fisheries_net |
4.778 |
578096 |
8.264e-06 |
1 |
|
| fisheries_dredge |
-125.8 |
15207593 |
-8.275e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
231 |
55.9 |
212 |
39.3 |
35.2 |
45.5 |
49.4 |
181 |
185 |
69.6 |
223 |
7.63 |
9.47 |
2.29 |
79.5 |

Caprella septentrionalis
## SDM for: caprella_septentrionalis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.63
## Tjur's pseudo-R2 is: 0.53
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-15.34 |
7.501 |
-2.045 |
0.0409 |
* |
| om |
-4.194 |
2.9 |
-1.446 |
0.1481 |
|
| gravel |
2.214 |
1.629 |
1.359 |
0.1741 |
|
| silt |
-2.769 |
2.096 |
-1.321 |
0.1863 |
|
| clay |
3.987 |
2.45 |
1.627 |
0.1037 |
|
| arsenic |
-6.901 |
4.837 |
-1.427 |
0.1536 |
|
| cadmium |
7.771 |
4.227 |
1.839 |
0.06597 |
|
| copper |
11.11 |
5.789 |
1.918 |
0.05509 |
|
| iron |
-15.88 |
8.848 |
-1.795 |
0.07261 |
|
| manganese |
0.05921 |
2.965 |
0.01997 |
0.9841 |
|
| mercury |
1.467 |
1.957 |
0.7496 |
0.4535 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.73 |
2.6 |
2.47 |
2.1 |
2.19 |
5.05 |
8.16 |
7.06 |
2.19 |
1.57 |

Influence indices
## McFadden's pseudo-R2 is: -7.28
## Tjur's pseudo-R2 is: 0.47
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.078e+15 |
7496963 |
-277184763 |
0 |
* * * |
| aquaculture |
3.252e+13 |
66771996 |
487077 |
0 |
* * * |
| city |
-4.966e+12 |
60515998 |
-82064 |
0 |
* * * |
| dredging_collect |
-8.073e+13 |
47886179 |
-1685846 |
0 |
* * * |
| dredging_dump |
2.529e+15 |
55589796 |
45499743 |
0 |
* * * |
| industry |
-4.734e+14 |
30193191 |
-15679140 |
0 |
* * * |
| shipping_mooring |
1.659e+13 |
51224704 |
323856 |
0 |
* * * |
| shipping_traffic |
-6.103e+14 |
22815885 |
-26747331 |
0 |
* * * |
| sewers_rain |
2.196e+15 |
67164046 |
32693463 |
0 |
* * * |
| sewers_waste |
-1.971e+15 |
90359677 |
-21810783 |
0 |
* * * |
| wharves_city |
-2.639e+14 |
72465526 |
-3642097 |
0 |
* * * |
| wharves_industry |
-2.066e+15 |
79067645 |
-26124635 |
0 |
* * * |
| fisheries_trap |
-3.869e+14 |
7277539 |
-53166922 |
0 |
* * * |
| fisheries_trawl |
-4.466e+14 |
8821984 |
-50626003 |
0 |
* * * |
| fisheries_net |
9.012e+13 |
7219163 |
12483989 |
0 |
* * * |
| fisheries_dredge |
-2.551e+14 |
19454028 |
-13112042 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Chaetodermatida
## SDM for: chaetodermatida
Abiotic parameters
## McFadden's pseudo-R2 is: 0.15
## Tjur's pseudo-R2 is: 0.11
## Pearson's pseudo-R2 is: 0.1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-3.613 |
2.483 |
-1.455 |
0.1457 |
|
| om |
-0.0259 |
0.7373 |
-0.03512 |
0.972 |
|
| gravel |
-0.5285 |
0.5882 |
-0.8986 |
0.3689 |
|
| silt |
-0.1829 |
0.6298 |
-0.2904 |
0.7715 |
|
| clay |
-7.934 |
13.45 |
-0.5898 |
0.5553 |
|
| arsenic |
0.1186 |
0.8455 |
0.1403 |
0.8884 |
|
| cadmium |
-0.6027 |
0.6787 |
-0.8879 |
0.3746 |
|
| copper |
0.4939 |
0.7447 |
0.6632 |
0.5072 |
|
| iron |
-0.4932 |
0.7072 |
-0.6974 |
0.4855 |
|
| manganese |
-0.9569 |
1.209 |
-0.7918 |
0.4285 |
|
| mercury |
0.08703 |
0.6661 |
0.1306 |
0.8961 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.67 |
1.11 |
1.71 |
1.02 |
1.55 |
1.63 |
1.9 |
1.63 |
1.99 |
1.5 |

Influence indices
## McFadden's pseudo-R2 is: 0.24
## Tjur's pseudo-R2 is: 0.2
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-66.5 |
6711 |
-0.009909 |
0.9921 |
|
| aquaculture |
4.806 |
5.27 |
0.912 |
0.3618 |
|
| city |
0.2363 |
4.736 |
0.0499 |
0.9602 |
|
| dredging_collect |
4.11 |
2.802 |
1.467 |
0.1425 |
|
| dredging_dump |
-2.921 |
4.22 |
-0.6922 |
0.4888 |
|
| industry |
0.8149 |
1.867 |
0.4365 |
0.6625 |
|
| shipping_mooring |
2.377 |
3.562 |
0.6675 |
0.5045 |
|
| shipping_traffic |
1.524 |
1.497 |
1.018 |
0.3088 |
|
| sewers_rain |
-0.7247 |
4.071 |
-0.178 |
0.8587 |
|
| sewers_waste |
3.27 |
7.203 |
0.454 |
0.6499 |
|
| wharves_city |
0.7321 |
6.646 |
0.1101 |
0.9123 |
|
| wharves_industry |
-5.104 |
4.52 |
-1.129 |
0.2589 |
|
| fisheries_trap |
-0.456 |
0.7794 |
-0.5851 |
0.5585 |
|
| fisheries_trawl |
-0.09704 |
0.2995 |
-0.3241 |
0.7459 |
|
| fisheries_net |
-660.9 |
69587 |
-0.009498 |
0.9924 |
|
| fisheries_dredge |
0.07327 |
0.7891 |
0.09286 |
0.926 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
12.4 |
12.8 |
7.44 |
10.6 |
4.47 |
9.58 |
4.05 |
11.7 |
18.8 |
17.2 |
11.2 |
1.07 |
1.38 |
1 |
1.74 |

Chionoecetes opilio
## SDM for: chionoecetes_opilio
Abiotic parameters
## McFadden's pseudo-R2 is: -6.59
## Tjur's pseudo-R2 is: 0.49
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.535e+15 |
7978909 |
-317727778 |
0 |
* * * |
| om |
9.975e+13 |
14257309 |
6996528 |
0 |
* * * |
| gravel |
8.095e+13 |
8714549 |
9288951 |
0 |
* * * |
| silt |
1.36e+15 |
15274819 |
89028485 |
0 |
* * * |
| clay |
-2.073e+15 |
21858102 |
-94855449 |
0 |
* * * |
| arsenic |
-4.63e+14 |
11951982 |
-38739076 |
0 |
* * * |
| cadmium |
-3.6e+14 |
10400421 |
-34610298 |
0 |
* * * |
| copper |
-9.333e+13 |
13687347 |
-6818572 |
0 |
* * * |
| iron |
-3.853e+14 |
9620115 |
-40047527 |
0 |
* * * |
| manganese |
8.513e+14 |
14288707 |
59580120 |
0 |
* * * |
| mercury |
-1.171e+15 |
12068606 |
-97010557 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-260.9 |
840224 |
-0.0003106 |
0.9998 |
|
| aquaculture |
-88.12 |
24921514 |
-3.536e-06 |
1 |
|
| city |
198.3 |
17367840 |
1.142e-05 |
1 |
|
| dredging_collect |
372.2 |
9125675 |
4.078e-05 |
1 |
|
| dredging_dump |
1092 |
20887879 |
5.23e-05 |
1 |
|
| industry |
271.9 |
1597109 |
0.0001703 |
0.9999 |
|
| shipping_mooring |
233 |
30038188 |
7.756e-06 |
1 |
|
| shipping_traffic |
-67.35 |
1616975 |
-4.165e-05 |
1 |
|
| sewers_rain |
779.6 |
9563099 |
8.152e-05 |
0.9999 |
|
| sewers_waste |
-959.8 |
12619665 |
-7.605e-05 |
0.9999 |
|
| wharves_city |
-443.6 |
19098772 |
-2.323e-05 |
1 |
|
| wharves_industry |
-1623 |
5167607 |
-0.0003141 |
0.9997 |
|
| fisheries_trap |
32.51 |
1417017 |
2.295e-05 |
1 |
|
| fisheries_trawl |
-217.9 |
2782677 |
-7.83e-05 |
0.9999 |
|
| fisheries_net |
11.9 |
392777 |
3.029e-05 |
1 |
|
| fisheries_dredge |
-117.4 |
3157382 |
-3.718e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
385 |
222 |
134 |
324 |
19.6 |
491 |
31.1 |
184 |
226 |
246 |
78.7 |
22.8 |
9.58 |
1.56 |
25.1 |

Chlamys islandica
## SDM for: chlamys_islandica
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-182.6 |
473133 |
-0.0003859 |
0.9997 |
|
| om |
-134.2 |
358347 |
-0.0003744 |
0.9997 |
|
| gravel |
-4.482 |
286106 |
-1.566e-05 |
1 |
|
| silt |
64.12 |
285563 |
0.0002245 |
0.9998 |
|
| clay |
-105.7 |
1398946 |
-7.555e-05 |
0.9999 |
|
| arsenic |
5.142 |
431968 |
1.19e-05 |
1 |
|
| cadmium |
-40.83 |
219759 |
-0.0001858 |
0.9999 |
|
| copper |
62.63 |
258197 |
0.0002426 |
0.9998 |
|
| iron |
9.686 |
117058 |
8.274e-05 |
0.9999 |
|
| manganese |
-19.25 |
328394 |
-5.861e-05 |
1 |
|
| mercury |
-69.44 |
301137 |
-0.0002306 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.88 |
1.28 |
4.22 |
1.51 |
3.79 |
3.13 |
4.32 |
1.92 |
4.01 |
3.3 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-35.16 |
72323 |
-0.0004862 |
0.9996 |
|
| aquaculture |
-24 |
1358385 |
-1.767e-05 |
1 |
|
| city |
-12.59 |
1297078 |
-9.708e-06 |
1 |
|
| dredging_collect |
-14.92 |
851895 |
-1.751e-05 |
1 |
|
| dredging_dump |
-6.445 |
662645 |
-9.726e-06 |
1 |
|
| industry |
11.9 |
525131 |
2.266e-05 |
1 |
|
| shipping_mooring |
-27.69 |
1438980 |
-1.924e-05 |
1 |
|
| shipping_traffic |
-18.11 |
338086 |
-5.356e-05 |
1 |
|
| sewers_rain |
-19.75 |
1022136 |
-1.932e-05 |
1 |
|
| sewers_waste |
24.54 |
1349796 |
1.818e-05 |
1 |
|
| wharves_city |
28.32 |
1807498 |
1.567e-05 |
1 |
|
| wharves_industry |
31.49 |
777801 |
4.048e-05 |
1 |
|
| fisheries_trap |
-0.6731 |
83030 |
-8.106e-06 |
1 |
|
| fisheries_trawl |
10.88 |
67868 |
0.0001604 |
0.9999 |
|
| fisheries_net |
3.765 |
70648 |
5.329e-05 |
1 |
|
| fisheries_dredge |
0.6407 |
190947 |
3.355e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
27.5 |
18.2 |
14.2 |
11 |
9.45 |
23.7 |
5.79 |
15.1 |
21.9 |
25.4 |
12.8 |
1.63 |
4.13 |
1.25 |
2.35 |

Chone sp
## SDM for: chone_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-645.8 |
3768764 |
-0.0001714 |
0.9999 |
|
| om |
-266.9 |
611132 |
-0.0004367 |
0.9997 |
|
| gravel |
-134.3 |
743421 |
-0.0001806 |
0.9999 |
|
| silt |
19.72 |
357831 |
5.512e-05 |
1 |
|
| clay |
1.933 |
19767865 |
9.78e-08 |
1 |
|
| arsenic |
-116.4 |
470332 |
-0.0002475 |
0.9998 |
|
| cadmium |
302.8 |
679239 |
0.0004459 |
0.9996 |
|
| copper |
422 |
875479 |
0.000482 |
0.9996 |
|
| iron |
-592.8 |
1397448 |
-0.0004242 |
0.9997 |
|
| manganese |
-137.3 |
757012 |
-0.0001814 |
0.9999 |
|
| mercury |
60.15 |
198975 |
0.0003023 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.7 |
1.36 |
5.04 |
1.12 |
8.45 |
18.9 |
29 |
29.7 |
2.96 |
2.61 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-71.01 |
140791 |
-0.0005043 |
0.9996 |
|
| aquaculture |
1.465 |
1423813 |
1.029e-06 |
1 |
|
| city |
-161.8 |
1043763 |
-0.000155 |
0.9999 |
|
| dredging_collect |
3.309 |
1272201 |
2.601e-06 |
1 |
|
| dredging_dump |
-26.73 |
1302138 |
-2.053e-05 |
1 |
|
| industry |
74.63 |
725722 |
0.0001028 |
0.9999 |
|
| shipping_mooring |
-18.84 |
2029726 |
-9.282e-06 |
1 |
|
| shipping_traffic |
-73.09 |
1305542 |
-5.599e-05 |
1 |
|
| sewers_rain |
30 |
1337511 |
2.243e-05 |
1 |
|
| sewers_waste |
84.55 |
1870889 |
4.519e-05 |
1 |
|
| wharves_city |
187 |
1966847 |
9.506e-05 |
0.9999 |
|
| wharves_industry |
-75.07 |
1924373 |
-3.901e-05 |
1 |
|
| fisheries_trap |
7.911 |
79350 |
9.97e-05 |
0.9999 |
|
| fisheries_trawl |
4.787 |
214578 |
2.231e-05 |
1 |
|
| fisheries_net |
1.25 |
121769 |
1.026e-05 |
1 |
|
| fisheries_dredge |
0.9453 |
383211 |
2.467e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.6 |
22.8 |
26.7 |
27.9 |
15 |
24.3 |
26.1 |
16 |
21.8 |
48.2 |
38.9 |
5.76 |
3.25 |
1.3 |
2.72 |

Ciliatocardium ciliatum
## SDM for: ciliatocardium_ciliatum
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-264.6 |
440426 |
-0.0006009 |
0.9995 |
|
| om |
168.8 |
326096 |
0.0005176 |
0.9996 |
|
| gravel |
-8.553 |
39461 |
-0.0002167 |
0.9998 |
|
| silt |
-257.5 |
432674 |
-0.0005951 |
0.9995 |
|
| clay |
236.3 |
803942 |
0.0002939 |
0.9998 |
|
| arsenic |
92.71 |
416591 |
0.0002225 |
0.9998 |
|
| cadmium |
-68.37 |
212492 |
-0.0003217 |
0.9997 |
|
| copper |
83.83 |
182890 |
0.0004584 |
0.9996 |
|
| iron |
-85.57 |
226102 |
-0.0003785 |
0.9997 |
|
| manganese |
-162.1 |
326948 |
-0.0004957 |
0.9996 |
|
| mercury |
51.08 |
201213 |
0.0002539 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
8.25 |
1.91 |
7.93 |
1.17 |
5.35 |
6.21 |
5.99 |
4.33 |
5.48 |
4.37 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-57.8 |
158431 |
-0.0003648 |
0.9997 |
|
| aquaculture |
-141 |
2137836 |
-6.597e-05 |
0.9999 |
|
| city |
-30.51 |
1621045 |
-1.882e-05 |
1 |
|
| dredging_collect |
-20.2 |
3567286 |
-5.662e-06 |
1 |
|
| dredging_dump |
188 |
2816576 |
6.676e-05 |
0.9999 |
|
| industry |
27.12 |
954077 |
2.842e-05 |
1 |
|
| shipping_mooring |
-129 |
1322614 |
-9.751e-05 |
0.9999 |
|
| shipping_traffic |
-21.35 |
600520 |
-3.555e-05 |
1 |
|
| sewers_rain |
134.7 |
2486394 |
5.417e-05 |
1 |
|
| sewers_waste |
-166.5 |
3425909 |
-4.861e-05 |
1 |
|
| wharves_city |
18.52 |
2582920 |
7.171e-06 |
1 |
|
| wharves_industry |
-126.8 |
5303166 |
-2.392e-05 |
1 |
|
| fisheries_trap |
-1.558 |
105641 |
-1.475e-05 |
1 |
|
| fisheries_trawl |
-11.89 |
742876 |
-1.601e-05 |
1 |
|
| fisheries_net |
0.1052 |
116782 |
9.007e-07 |
1 |
|
| fisheries_dredge |
-29.61 |
809373 |
-3.658e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
37.1 |
25 |
60.6 |
45.3 |
13.4 |
21.1 |
7.47 |
29.6 |
50.7 |
39.9 |
84.6 |
1.26 |
16.5 |
1.25 |
5.39 |

Cirripedia
## SDM for: cirripedia
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-44.22 |
75880 |
-0.0005827 |
0.9995 |
|
| om |
-2.639 |
151441 |
-1.742e-05 |
1 |
|
| gravel |
1.01 |
63895 |
1.581e-05 |
1 |
|
| silt |
-3.933 |
139094 |
-2.828e-05 |
1 |
|
| clay |
6.164 |
162601 |
3.791e-05 |
1 |
|
| arsenic |
-8.429 |
294890 |
-2.858e-05 |
1 |
|
| cadmium |
5.174 |
98580 |
5.248e-05 |
1 |
|
| copper |
-13.34 |
310807 |
-4.292e-05 |
1 |
|
| iron |
-0.05162 |
54023 |
-9.556e-07 |
1 |
|
| manganese |
18.36 |
185661 |
9.89e-05 |
0.9999 |
|
| mercury |
-5.739 |
218160 |
-2.63e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.31 |
1.25 |
3.05 |
1.13 |
3.36 |
3.87 |
7.01 |
1.59 |
3.89 |
3.24 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-60.17 |
130978 |
-0.0004594 |
0.9996 |
|
| aquaculture |
31.5 |
1380961 |
2.281e-05 |
1 |
|
| city |
37.95 |
1785017 |
2.126e-05 |
1 |
|
| dredging_collect |
63.15 |
742538 |
8.504e-05 |
0.9999 |
|
| dredging_dump |
-63.26 |
1722768 |
-3.672e-05 |
1 |
|
| industry |
31.56 |
703594 |
4.485e-05 |
1 |
|
| shipping_mooring |
32.37 |
703891 |
4.598e-05 |
1 |
|
| shipping_traffic |
8 |
868298 |
9.213e-06 |
1 |
|
| sewers_rain |
-15.99 |
2149623 |
-7.439e-06 |
1 |
|
| sewers_waste |
39.44 |
2782990 |
1.417e-05 |
1 |
|
| wharves_city |
-59.64 |
1583439 |
-3.766e-05 |
1 |
|
| wharves_industry |
-47.69 |
2079769 |
-2.293e-05 |
1 |
|
| fisheries_trap |
16.64 |
75018 |
0.0002218 |
0.9998 |
|
| fisheries_trawl |
0.4976 |
298217 |
1.669e-06 |
1 |
|
| fisheries_net |
2.283 |
109978 |
2.076e-05 |
1 |
|
| fisheries_dredge |
-17.06 |
375642 |
-4.541e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
23 |
32.6 |
12.8 |
29.3 |
9.43 |
10.4 |
13 |
31.9 |
46.4 |
31.8 |
35.8 |
2.97 |
3.33 |
1.17 |
2.71 |

Cistenides granulata
## SDM for: cistenides_granulata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.21
## Tjur's pseudo-R2 is: 0.19
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.347 |
0.8041 |
-2.918 |
0.003518 |
* * |
| om |
0.3262 |
0.6812 |
0.4788 |
0.6321 |
|
| gravel |
0.01923 |
0.3094 |
0.06214 |
0.9504 |
|
| silt |
-0.3121 |
0.5791 |
-0.5389 |
0.5899 |
|
| clay |
-1.299 |
3.723 |
-0.3489 |
0.7272 |
|
| arsenic |
0.6119 |
0.6969 |
0.8781 |
0.3799 |
|
| cadmium |
-0.3133 |
0.5337 |
-0.5869 |
0.5573 |
|
| copper |
-1.442 |
0.8214 |
-1.756 |
0.07911 |
|
| iron |
-0.2667 |
0.4162 |
-0.6407 |
0.5217 |
|
| manganese |
-0.1623 |
1.029 |
-0.1578 |
0.8746 |
|
| mercury |
0.134 |
0.6368 |
0.2105 |
0.8333 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.71 |
1.2 |
1.81 |
1.1 |
1.5 |
1.5 |
1.85 |
1.38 |
2.02 |
1.44 |

Influence indices
## McFadden's pseudo-R2 is: 0.38
## Tjur's pseudo-R2 is: 0.38
## Pearson's pseudo-R2 is: 0.39
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-75.65 |
10097 |
-0.007492 |
0.994 |
|
| aquaculture |
-0.8221 |
3.684 |
-0.2232 |
0.8234 |
|
| city |
-21.6 |
18.16 |
-1.19 |
0.2342 |
|
| dredging_collect |
-0.8741 |
3.352 |
-0.2608 |
0.7942 |
|
| dredging_dump |
-8.307 |
4.277 |
-1.942 |
0.05213 |
|
| industry |
-0.02645 |
2.008 |
-0.01317 |
0.9895 |
|
| shipping_mooring |
9.744 |
7.712 |
1.264 |
0.2064 |
|
| shipping_traffic |
1.619 |
1.426 |
1.135 |
0.2564 |
|
| sewers_rain |
1.525 |
3.985 |
0.3827 |
0.7019 |
|
| sewers_waste |
-12.43 |
9.685 |
-1.284 |
0.1992 |
|
| wharves_city |
17.15 |
14.9 |
1.151 |
0.2498 |
|
| wharves_industry |
8.75 |
4.704 |
1.86 |
0.06289 |
|
| fisheries_trap |
1.019 |
0.6782 |
1.503 |
0.133 |
|
| fisheries_trawl |
0.1398 |
0.3297 |
0.4239 |
0.6716 |
|
| fisheries_net |
-727.7 |
104696 |
-0.00695 |
0.9945 |
|
| fisheries_dredge |
-0.476 |
1.064 |
-0.4473 |
0.6547 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.5 |
43.6 |
9.14 |
10.7 |
4.61 |
21 |
3.64 |
9.91 |
23.2 |
37.6 |
12.1 |
1.19 |
1.53 |
1 |
1.84 |

Cossura longocirrata
## SDM for: cossura_longocirrata
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1045 |
1790518 |
-0.0005837 |
0.9995 |
|
| om |
-204.3 |
4309757 |
-4.741e-05 |
1 |
|
| gravel |
335.4 |
1479060 |
0.0002267 |
0.9998 |
|
| silt |
748.7 |
2785928 |
0.0002688 |
0.9998 |
|
| clay |
133.7 |
3252924 |
4.111e-05 |
1 |
|
| arsenic |
-28.47 |
882762 |
-3.225e-05 |
1 |
|
| cadmium |
360.8 |
616981 |
0.0005848 |
0.9995 |
|
| copper |
-189.8 |
5559395 |
-3.414e-05 |
1 |
|
| iron |
392.3 |
4401443 |
8.913e-05 |
0.9999 |
|
| manganese |
-120 |
2711133 |
-4.428e-05 |
1 |
|
| mercury |
26.94 |
3069482 |
8.777e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
102 |
6.38 |
36.1 |
3.06 |
29.4 |
15.1 |
194 |
82.1 |
48.6 |
47.9 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-74.33 |
124910 |
-0.000595 |
0.9995 |
|
| aquaculture |
-34.27 |
2021987 |
-1.695e-05 |
1 |
|
| city |
-154.8 |
1086070 |
-0.0001426 |
0.9999 |
|
| dredging_collect |
125.4 |
1054178 |
0.0001189 |
0.9999 |
|
| dredging_dump |
176.1 |
1367781 |
0.0001288 |
0.9999 |
|
| industry |
17.03 |
1424404 |
1.196e-05 |
1 |
|
| shipping_mooring |
136 |
1062050 |
0.000128 |
0.9999 |
|
| shipping_traffic |
-64.24 |
776605 |
-8.272e-05 |
0.9999 |
|
| sewers_rain |
198.2 |
938698 |
0.0002111 |
0.9998 |
|
| sewers_waste |
-284.7 |
1872163 |
-0.0001521 |
0.9999 |
|
| wharves_city |
70.9 |
1472122 |
4.816e-05 |
1 |
|
| wharves_industry |
-296.6 |
1886161 |
-0.0001573 |
0.9999 |
|
| fisheries_trap |
6.957 |
97097 |
7.165e-05 |
0.9999 |
|
| fisheries_trawl |
7.799 |
101911 |
7.652e-05 |
0.9999 |
|
| fisheries_net |
0.767 |
110307 |
6.953e-06 |
1 |
|
| fisheries_dredge |
-15.54 |
256295 |
-6.063e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
39.8 |
16.9 |
20.1 |
30.4 |
29.3 |
18.3 |
17.8 |
16.2 |
25.8 |
29.5 |
33.4 |
1.39 |
1.94 |
1.19 |
2.04 |

Crassicorophium bonellii
## SDM for: crassicorophium_bonellii
Abiotic parameters
## McFadden's pseudo-R2 is: 0.59
## Tjur's pseudo-R2 is: 0.46
## Pearson's pseudo-R2 is: 0.45
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-273.3 |
104039 |
-0.002627 |
0.9979 |
|
| om |
-2.757 |
3.6 |
-0.7656 |
0.4439 |
|
| gravel |
-0.4384 |
1.931 |
-0.2271 |
0.8203 |
|
| silt |
1.549 |
3.32 |
0.4667 |
0.6407 |
|
| clay |
-1450 |
567722 |
-0.002554 |
0.998 |
|
| arsenic |
0.9015 |
2.878 |
0.3132 |
0.7541 |
|
| cadmium |
-2.437 |
3.008 |
-0.8101 |
0.4179 |
|
| copper |
-0.6514 |
3.089 |
-0.2109 |
0.833 |
|
| iron |
-2.104 |
5.235 |
-0.4019 |
0.6878 |
|
| manganese |
5.197 |
4.167 |
1.247 |
0.2123 |
|
| mercury |
-3 |
4.614 |
-0.6503 |
0.5155 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.89 |
1.18 |
2.96 |
1 |
2.48 |
3.01 |
3.71 |
3.85 |
4.52 |
2.89 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-62.84 |
127641 |
-0.0004923 |
0.9996 |
|
| aquaculture |
140.5 |
1256041 |
0.0001119 |
0.9999 |
|
| city |
-27.7 |
1142978 |
-2.424e-05 |
1 |
|
| dredging_collect |
-15.33 |
1257400 |
-1.219e-05 |
1 |
|
| dredging_dump |
-83.28 |
753932 |
-0.0001105 |
0.9999 |
|
| industry |
-63.59 |
495922 |
-0.0001282 |
0.9999 |
|
| shipping_mooring |
9.881 |
1196063 |
8.262e-06 |
1 |
|
| shipping_traffic |
6.234 |
625365 |
9.969e-06 |
1 |
|
| sewers_rain |
-210.1 |
1528840 |
-0.0001375 |
0.9999 |
|
| sewers_waste |
308.3 |
1916854 |
0.0001608 |
0.9999 |
|
| wharves_city |
42.31 |
1324143 |
3.196e-05 |
1 |
|
| wharves_industry |
129.9 |
1778324 |
7.306e-05 |
0.9999 |
|
| fisheries_trap |
-20.17 |
807226 |
-2.499e-05 |
1 |
|
| fisheries_trawl |
9.658 |
171792 |
5.622e-05 |
1 |
|
| fisheries_net |
10.06 |
183184 |
5.493e-05 |
1 |
|
| fisheries_dredge |
23.38 |
160823 |
0.0001454 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.1 |
18.2 |
23.3 |
15 |
8.9 |
18.6 |
12.3 |
32.7 |
38.8 |
20.1 |
35.8 |
4.16 |
2.89 |
1.97 |
1.74 |

Crenella decussata
## SDM for: crenella_decussata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.78
## Tjur's pseudo-R2 is: 0.76
## Pearson's pseudo-R2 is: 0.76
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-29.8 |
22.83 |
-1.305 |
0.1918 |
|
| om |
-13.5 |
11.14 |
-1.212 |
0.2255 |
|
| gravel |
3.567 |
2.154 |
1.656 |
0.0977 |
|
| silt |
2.274 |
2.76 |
0.8238 |
0.41 |
|
| clay |
5.278 |
5.165 |
1.022 |
0.3068 |
|
| arsenic |
-15.11 |
16.08 |
-0.94 |
0.3472 |
|
| cadmium |
0.3276 |
2.559 |
0.128 |
0.8981 |
|
| copper |
2.786 |
4.842 |
0.5754 |
0.565 |
|
| iron |
1.539 |
3.441 |
0.4472 |
0.6548 |
|
| manganese |
-10.59 |
17.55 |
-0.6033 |
0.5463 |
|
| mercury |
-6.099 |
6.397 |
-0.9533 |
0.3404 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.02 |
1.28 |
3.16 |
2.18 |
2.18 |
2.31 |
2.47 |
2.55 |
3.93 |
3.83 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-620.2 |
1458329 |
-0.0004253 |
0.9997 |
|
| aquaculture |
-176.6 |
6367453 |
-2.774e-05 |
1 |
|
| city |
405.7 |
9846434 |
4.121e-05 |
1 |
|
| dredging_collect |
665.1 |
3658749 |
0.0001818 |
0.9999 |
|
| dredging_dump |
213.5 |
2723940 |
7.839e-05 |
0.9999 |
|
| industry |
-308.9 |
2495149 |
-0.0001238 |
0.9999 |
|
| shipping_mooring |
1023 |
9593856 |
0.0001066 |
0.9999 |
|
| shipping_traffic |
285 |
1342746 |
0.0002122 |
0.9998 |
|
| sewers_rain |
1324 |
4396795 |
0.0003011 |
0.9998 |
|
| sewers_waste |
-3048 |
7405218 |
-0.0004116 |
0.9997 |
|
| wharves_city |
-1169 |
8128917 |
-0.0001438 |
0.9999 |
|
| wharves_industry |
-332.7 |
2059690 |
-0.0001615 |
0.9999 |
|
| fisheries_trap |
31.53 |
169080 |
0.0001865 |
0.9999 |
|
| fisheries_trawl |
-44.55 |
871479 |
-5.112e-05 |
1 |
|
| fisheries_net |
3.088 |
6463619 |
4.777e-07 |
1 |
|
| fisheries_dredge |
-96.21 |
497787 |
-0.0001933 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
109 |
163 |
68.8 |
45.2 |
31.1 |
159 |
31.9 |
102 |
124 |
141 |
38.4 |
1.95 |
9.03 |
1.28 |
4.94 |

Cumacea
## SDM for: cumacea
Abiotic parameters
## McFadden's pseudo-R2 is: 0.17
## Tjur's pseudo-R2 is: 0.02
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-128 |
42198 |
-0.003033 |
0.9976 |
|
| om |
-0.6183 |
2.356 |
-0.2624 |
0.793 |
|
| gravel |
-30.29 |
28362 |
-0.001068 |
0.9991 |
|
| silt |
-0.3444 |
2.726 |
-0.1264 |
0.8995 |
|
| clay |
-627.2 |
226212 |
-0.002772 |
0.9978 |
|
| arsenic |
0.3619 |
1.283 |
0.2821 |
0.7779 |
|
| cadmium |
-0.9563 |
1.739 |
-0.5499 |
0.5824 |
|
| copper |
0.1673 |
1.959 |
0.08537 |
0.932 |
|
| iron |
-0.652 |
1.968 |
-0.3313 |
0.7405 |
|
| manganese |
1.86 |
2.565 |
0.725 |
0.4685 |
|
| mercury |
-0.5423 |
2.105 |
-0.2577 |
0.7967 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.01 |
1 |
2.7 |
1 |
1.59 |
1.82 |
2.33 |
1.76 |
3.08 |
2.5 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-61.82 |
145646 |
-0.0004245 |
0.9997 |
|
| aquaculture |
203.4 |
1745366 |
0.0001166 |
0.9999 |
|
| city |
15.68 |
1548200 |
1.013e-05 |
1 |
|
| dredging_collect |
38.44 |
812731 |
4.73e-05 |
1 |
|
| dredging_dump |
-70.16 |
1376180 |
-5.098e-05 |
1 |
|
| industry |
-76.12 |
506779 |
-0.0001502 |
0.9999 |
|
| shipping_mooring |
104.7 |
1157996 |
9.045e-05 |
0.9999 |
|
| shipping_traffic |
28.66 |
613197 |
4.673e-05 |
1 |
|
| sewers_rain |
-100.6 |
1264314 |
-7.96e-05 |
0.9999 |
|
| sewers_waste |
171.1 |
1535284 |
0.0001114 |
0.9999 |
|
| wharves_city |
-41.19 |
1465481 |
-2.81e-05 |
1 |
|
| wharves_industry |
30.04 |
1519005 |
1.978e-05 |
1 |
|
| fisheries_trap |
3.99 |
92121 |
4.331e-05 |
1 |
|
| fisheries_trawl |
-9.307 |
245987 |
-3.784e-05 |
1 |
|
| fisheries_net |
-1.207 |
106662 |
-1.131e-05 |
1 |
|
| fisheries_dredge |
-34.73 |
1003265 |
-3.461e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
24.7 |
25.4 |
15.2 |
23.5 |
7.39 |
18.6 |
9.61 |
29.4 |
28.7 |
23.5 |
29 |
1.29 |
2.59 |
1.09 |
5.14 |

Cyclocardia borealis
## SDM for: cyclocardia_borealis
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Cyrtodaria siliqua
## SDM for: cyrtodaria_siliqua
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-748.1 |
1541191 |
-0.0004854 |
0.9996 |
|
| om |
-388.7 |
893766 |
-0.0004349 |
0.9997 |
|
| gravel |
-92.15 |
593759 |
-0.0001552 |
0.9999 |
|
| silt |
31.2 |
353157 |
8.834e-05 |
0.9999 |
|
| clay |
126 |
341223 |
0.0003691 |
0.9997 |
|
| arsenic |
-264.5 |
1888482 |
-0.00014 |
0.9999 |
|
| cadmium |
123.6 |
545310 |
0.0002267 |
0.9998 |
|
| copper |
165.2 |
550194 |
0.0003003 |
0.9998 |
|
| iron |
85.97 |
228157 |
0.0003768 |
0.9997 |
|
| manganese |
-287.9 |
1147693 |
-0.0002508 |
0.9998 |
|
| mercury |
-320.8 |
1170193 |
-0.0002741 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
8.2 |
1.5 |
3.21 |
3.34 |
7.22 |
8.01 |
7.1 |
11.9 |
5.86 |
10.4 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-118.3 |
403879 |
-0.0002928 |
0.9998 |
|
| aquaculture |
-416 |
11032501 |
-3.771e-05 |
1 |
|
| city |
120.9 |
13647458 |
8.857e-06 |
1 |
|
| dredging_collect |
-248.8 |
3656150 |
-6.806e-05 |
0.9999 |
|
| dredging_dump |
176.9 |
5986096 |
2.955e-05 |
1 |
|
| industry |
99.44 |
3294415 |
3.018e-05 |
1 |
|
| shipping_mooring |
-268.5 |
11695926 |
-2.296e-05 |
1 |
|
| shipping_traffic |
-31.74 |
5292898 |
-5.997e-06 |
1 |
|
| sewers_rain |
293 |
6230508 |
4.703e-05 |
1 |
|
| sewers_waste |
-440.9 |
4792057 |
-9.201e-05 |
0.9999 |
|
| wharves_city |
-66.45 |
16516282 |
-4.023e-06 |
1 |
|
| wharves_industry |
143.9 |
4616027 |
3.118e-05 |
1 |
|
| fisheries_trap |
-23.46 |
598903 |
-3.917e-05 |
1 |
|
| fisheries_trawl |
19.14 |
487639 |
3.924e-05 |
1 |
|
| fisheries_net |
9.298 |
313267 |
2.968e-05 |
1 |
|
| fisheries_dredge |
82.97 |
1415017 |
5.864e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
101 |
188 |
48.5 |
80.6 |
31.8 |
94.8 |
71.2 |
44.7 |
31.9 |
253 |
64 |
2.79 |
3.71 |
2.04 |
11 |

Diastylis rathkei
## SDM for: diastylis_rathkei
Abiotic parameters
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.24
## Pearson's pseudo-R2 is: 0.22
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-4.075 |
1.011 |
-4.031 |
5.551e-05 |
* * * |
| om |
-2.186 |
1.047 |
-2.088 |
0.03678 |
* |
| gravel |
1.086 |
0.5487 |
1.98 |
0.04775 |
* |
| silt |
2.624 |
1.2 |
2.186 |
0.02883 |
* |
| clay |
-1.298 |
1.539 |
-0.8431 |
0.3992 |
|
| arsenic |
-1.44 |
1.627 |
-0.8853 |
0.376 |
|
| cadmium |
-0.4527 |
0.7203 |
-0.6286 |
0.5296 |
|
| copper |
0.895 |
0.9132 |
0.98 |
0.3271 |
|
| iron |
0.8496 |
0.5164 |
1.645 |
0.0999 |
|
| manganese |
1.016 |
1.383 |
0.7346 |
0.4626 |
|
| mercury |
-1.933 |
1.12 |
-1.727 |
0.08417 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.6 |
1.76 |
2.56 |
1.09 |
1.9 |
1.66 |
1.88 |
1.56 |
2.4 |
1.69 |

Influence indices
## McFadden's pseudo-R2 is: 0.22
## Tjur's pseudo-R2 is: 0.17
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-45.3 |
11414 |
-0.003969 |
0.9968 |
|
| aquaculture |
2.949 |
4.948 |
0.596 |
0.5512 |
|
| city |
0.656 |
4.651 |
0.1411 |
0.8878 |
|
| dredging_collect |
-3.011 |
3.831 |
-0.786 |
0.4319 |
|
| dredging_dump |
1.417 |
4.441 |
0.319 |
0.7497 |
|
| industry |
-2.65 |
2.041 |
-1.298 |
0.1943 |
|
| shipping_mooring |
-1.062 |
3.997 |
-0.2658 |
0.7904 |
|
| shipping_traffic |
0.5321 |
1.399 |
0.3802 |
0.7038 |
|
| sewers_rain |
-4.253 |
5.296 |
-0.8032 |
0.4219 |
|
| sewers_waste |
3.569 |
7.306 |
0.4885 |
0.6252 |
|
| wharves_city |
-1.439 |
5.072 |
-0.2836 |
0.7767 |
|
| wharves_industry |
4.919 |
5.979 |
0.8228 |
0.4106 |
|
| fisheries_trap |
0.04892 |
0.4417 |
0.1108 |
0.9118 |
|
| fisheries_trawl |
-0.404 |
0.957 |
-0.4221 |
0.6729 |
|
| fisheries_net |
-431.6 |
118352 |
-0.003647 |
0.9971 |
|
| fisheries_dredge |
-3.246 |
2.198 |
-1.477 |
0.1397 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.2 |
12.9 |
10.3 |
12.1 |
4.06 |
9.18 |
3.33 |
11.4 |
16.2 |
14.8 |
15.8 |
1.12 |
2.22 |
1 |
2.2 |

Diastylis sculpta
## SDM for: diastylis_sculpta
Abiotic parameters
## McFadden's pseudo-R2 is: 0.39
## Tjur's pseudo-R2 is: 0.29
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-5.761 |
2.047 |
-2.815 |
0.004881 |
* * |
| om |
-2.685 |
1.87 |
-1.436 |
0.151 |
|
| gravel |
-0.3701 |
1.733 |
-0.2136 |
0.8309 |
|
| silt |
2.21 |
1.432 |
1.542 |
0.123 |
|
| clay |
-0.9117 |
1.524 |
-0.5983 |
0.5496 |
|
| arsenic |
-2.924 |
3.153 |
-0.9273 |
0.3538 |
|
| cadmium |
-1.7 |
1.554 |
-1.094 |
0.2738 |
|
| copper |
3.982 |
1.801 |
2.211 |
0.02703 |
* |
| iron |
-0.997 |
1.617 |
-0.6166 |
0.5375 |
|
| manganese |
0.2236 |
2.921 |
0.07655 |
0.939 |
|
| mercury |
-2.189 |
1.617 |
-1.353 |
0.176 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.88 |
1.22 |
1.97 |
1.14 |
2.09 |
2.45 |
3.24 |
2.37 |
3.11 |
1.78 |

Influence indices
## McFadden's pseudo-R2 is: -8.94
## Tjur's pseudo-R2 is: 0
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.398e+15 |
7496963 |
-319830134 |
0 |
* * * |
| aquaculture |
-4.774e+13 |
66771996 |
-714971 |
0 |
* * * |
| city |
-3.179e+15 |
60515998 |
-52525110 |
0 |
* * * |
| dredging_collect |
1.988e+14 |
47886179 |
4152477 |
0 |
* * * |
| dredging_dump |
-2.275e+15 |
55589796 |
-40929096 |
0 |
* * * |
| industry |
-2.388e+14 |
30193191 |
-7909223 |
0 |
* * * |
| shipping_mooring |
1.399e+15 |
51224704 |
27302347 |
0 |
* * * |
| shipping_traffic |
-1.823e+15 |
22815885 |
-79887457 |
0 |
* * * |
| sewers_rain |
-1.824e+14 |
67164046 |
-2716297 |
0 |
* * * |
| sewers_waste |
-1.205e+15 |
90359677 |
-13337312 |
0 |
* * * |
| wharves_city |
4.18e+15 |
72465526 |
57680109 |
0 |
* * * |
| wharves_industry |
2.988e+15 |
79067645 |
37784780 |
0 |
* * * |
| fisheries_trap |
2.115e+13 |
7277539 |
2906037 |
0 |
* * * |
| fisheries_trawl |
2.704e+14 |
8821984 |
30650023 |
0 |
* * * |
| fisheries_net |
2.984e+14 |
7219163 |
41337019 |
0 |
* * * |
| fisheries_dredge |
-2.207e+14 |
19454028 |
-11342147 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Diastylis sp
## SDM for: diastylis_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.57
## Tjur's pseudo-R2 is: 0.23
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.82 |
13531 |
-0.001982 |
0.9984 |
|
| om |
1.916 |
5.64 |
0.3397 |
0.7341 |
|
| gravel |
-14.38 |
15078 |
-0.0009537 |
0.9992 |
|
| silt |
3.138 |
6.532 |
0.4805 |
0.6309 |
|
| clay |
-60.03 |
70639 |
-0.0008499 |
0.9993 |
|
| arsenic |
-3.082 |
23.26 |
-0.1325 |
0.8946 |
|
| cadmium |
1.466 |
4.203 |
0.3489 |
0.7272 |
|
| copper |
-0.5717 |
5.698 |
-0.1003 |
0.9201 |
|
| iron |
0.8747 |
3.056 |
0.2862 |
0.7747 |
|
| manganese |
-5.513 |
12.65 |
-0.4357 |
0.663 |
|
| mercury |
-3.01 |
5.84 |
-0.5153 |
0.6063 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.45 |
1 |
1.99 |
1 |
3.19 |
2.07 |
2.36 |
1.64 |
2 |
1.75 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-116.1 |
347694 |
-0.0003338 |
0.9997 |
|
| aquaculture |
-62.12 |
8744652 |
-7.104e-06 |
1 |
|
| city |
75.52 |
4601540 |
1.641e-05 |
1 |
|
| dredging_collect |
-243.4 |
8020177 |
-3.035e-05 |
1 |
|
| dredging_dump |
236.4 |
6990674 |
3.382e-05 |
1 |
|
| industry |
42.81 |
2759877 |
1.551e-05 |
1 |
|
| shipping_mooring |
-63.9 |
8e+06 |
-7.987e-06 |
1 |
|
| shipping_traffic |
-148.2 |
1241730 |
-0.0001193 |
0.9999 |
|
| sewers_rain |
235 |
6691602 |
3.511e-05 |
1 |
|
| sewers_waste |
-246.2 |
7680325 |
-3.205e-05 |
1 |
|
| wharves_city |
-112.3 |
6308112 |
-1.78e-05 |
1 |
|
| wharves_industry |
140.7 |
14723296 |
9.553e-06 |
1 |
|
| fisheries_trap |
9.174 |
723965 |
1.267e-05 |
1 |
|
| fisheries_trawl |
10.42 |
457603 |
2.277e-05 |
1 |
|
| fisheries_net |
4.843 |
474452 |
1.021e-05 |
1 |
|
| fisheries_dredge |
-98.64 |
2034794 |
-4.848e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
121 |
43.1 |
81 |
65.7 |
23.8 |
96.7 |
13.8 |
82.5 |
101 |
57.5 |
142 |
8.76 |
10.7 |
3.09 |
21.6 |

Echinarachnius parma
## SDM for: echinarachnius_parma
Abiotic parameters
## McFadden's pseudo-R2 is: 0.62
## Tjur's pseudo-R2 is: 0.63
## Pearson's pseudo-R2 is: 0.65
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-18.89 |
6.594 |
-2.865 |
0.004172 |
* * |
| om |
-6.29 |
2.743 |
-2.293 |
0.02183 |
* |
| gravel |
0.02614 |
1.03 |
0.02537 |
0.9798 |
|
| silt |
3.289 |
1.397 |
2.354 |
0.01856 |
* |
| clay |
0.6205 |
2.414 |
0.2571 |
0.7971 |
|
| arsenic |
-17.82 |
6.984 |
-2.551 |
0.01073 |
* |
| cadmium |
2.074 |
1.373 |
1.51 |
0.1309 |
|
| copper |
2.689 |
1.383 |
1.944 |
0.05195 |
|
| iron |
1.318 |
0.7683 |
1.716 |
0.08617 |
|
| manganese |
-7.79 |
3.967 |
-1.964 |
0.04959 |
* |
| mercury |
-5.603 |
2.383 |
-2.352 |
0.01869 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.48 |
1.07 |
2.49 |
1.07 |
1.79 |
2.94 |
1.91 |
2.71 |
3.61 |
2.61 |

Influence indices
## McFadden's pseudo-R2 is: 0.56
## Tjur's pseudo-R2 is: 0.54
## Pearson's pseudo-R2 is: 0.54
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-67.82 |
11393 |
-0.005953 |
0.9953 |
|
| aquaculture |
-2.188 |
6.429 |
-0.3403 |
0.7336 |
|
| city |
7.769 |
8.32 |
0.9337 |
0.3504 |
|
| dredging_collect |
2.362 |
4.245 |
0.5564 |
0.5779 |
|
| dredging_dump |
6.501 |
9.892 |
0.6572 |
0.511 |
|
| industry |
-11.41 |
5.226 |
-2.184 |
0.02899 |
* |
| shipping_mooring |
-1.655 |
6.445 |
-0.2568 |
0.7974 |
|
| shipping_traffic |
-2.284 |
1.961 |
-1.165 |
0.2441 |
|
| sewers_rain |
12.38 |
9.564 |
1.295 |
0.1955 |
|
| sewers_waste |
-27.36 |
14.87 |
-1.84 |
0.06572 |
|
| wharves_city |
-12.11 |
11.85 |
-1.021 |
0.3072 |
|
| wharves_industry |
9.522 |
6.437 |
1.479 |
0.139 |
|
| fisheries_trap |
0.5223 |
0.4497 |
1.161 |
0.2455 |
|
| fisheries_trawl |
0.6427 |
0.436 |
1.474 |
0.1404 |
|
| fisheries_net |
-614.1 |
118136 |
-0.005198 |
0.9959 |
|
| fisheries_dredge |
-2.804 |
1.682 |
-1.667 |
0.09552 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.85 |
21.6 |
10.8 |
25.5 |
9.68 |
12.1 |
4.49 |
19 |
25 |
31.7 |
15.3 |
1.51 |
1.73 |
1 |
2.36 |

Edotia montosa
## SDM for: edotia_montosa
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-146.9 |
360275 |
-0.0004078 |
0.9997 |
|
| om |
143.9 |
414409 |
0.0003472 |
0.9997 |
|
| gravel |
-52.99 |
318008 |
-0.0001666 |
0.9999 |
|
| silt |
-168.1 |
469961 |
-0.0003577 |
0.9997 |
|
| clay |
124 |
698292 |
0.0001776 |
0.9999 |
|
| arsenic |
-43.51 |
413629 |
-0.0001052 |
0.9999 |
|
| cadmium |
33.21 |
133483 |
0.0002488 |
0.9998 |
|
| copper |
45.85 |
318868 |
0.0001438 |
0.9999 |
|
| iron |
-118.3 |
520671 |
-0.0002272 |
0.9998 |
|
| manganese |
105.4 |
356874 |
0.0002954 |
0.9998 |
|
| mercury |
-63.9 |
424385 |
-0.0001506 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
6.44 |
6.61 |
5.52 |
1.14 |
6.04 |
2.78 |
8.54 |
8.14 |
7.47 |
5.57 |

Influence indices
## McFadden's pseudo-R2 is: 0.38
## Tjur's pseudo-R2 is: 0.22
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-164.3 |
84226 |
-0.001951 |
0.9984 |
|
| aquaculture |
-17.95 |
20.12 |
-0.892 |
0.3724 |
|
| city |
0.2005 |
9.749 |
0.02056 |
0.9836 |
|
| dredging_collect |
-6.436 |
11.96 |
-0.5379 |
0.5906 |
|
| dredging_dump |
-9.312 |
8.776 |
-1.061 |
0.2886 |
|
| industry |
7.693 |
8.381 |
0.9179 |
0.3587 |
|
| shipping_mooring |
-10.72 |
11.23 |
-0.9544 |
0.3399 |
|
| shipping_traffic |
-2.921 |
8.044 |
-0.3631 |
0.7165 |
|
| sewers_rain |
6.065 |
12.86 |
0.4715 |
0.6373 |
|
| sewers_waste |
-8.253 |
16.98 |
-0.4861 |
0.6269 |
|
| wharves_city |
6.759 |
15.86 |
0.4262 |
0.6699 |
|
| wharves_industry |
14.07 |
16.19 |
0.8693 |
0.3847 |
|
| fisheries_trap |
-12.17 |
14.27 |
-0.8529 |
0.3937 |
|
| fisheries_trawl |
-128.5 |
1002382 |
-0.0001282 |
0.9999 |
|
| fisheries_net |
-827.7 |
293735 |
-0.002818 |
0.9978 |
|
| fisheries_dredge |
-117.4 |
594522 |
-0.0001975 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
20.6 |
15.9 |
21.5 |
16.9 |
20.1 |
9.32 |
17.7 |
13.4 |
17.5 |
25 |
32 |
2.11 |
5.82 |
1 |
5.82 |

Ennucula tenuis
## SDM for: ennucula_tenuis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.29
## Tjur's pseudo-R2 is: 0.35
## Pearson's pseudo-R2 is: 0.37
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-0.4074 |
0.4181 |
-0.9744 |
0.3298 |
|
| om |
1.057 |
0.6095 |
1.735 |
0.08281 |
|
| gravel |
0.2424 |
0.3266 |
0.7424 |
0.4579 |
|
| silt |
0.202 |
0.6277 |
0.3218 |
0.7476 |
|
| clay |
-0.8662 |
1.835 |
-0.4719 |
0.637 |
|
| arsenic |
2.114 |
0.8527 |
2.479 |
0.01319 |
* |
| cadmium |
-2.316 |
0.7399 |
-3.13 |
0.001746 |
* * |
| copper |
0.03025 |
0.603 |
0.05018 |
0.96 |
|
| iron |
-0.08138 |
0.3688 |
-0.2207 |
0.8253 |
|
| manganese |
-2.155 |
0.8008 |
-2.691 |
0.007127 |
* * |
| mercury |
1.1 |
0.6251 |
1.76 |
0.07846 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.33 |
1.47 |
2.42 |
1.03 |
2.25 |
2.49 |
2.17 |
1.42 |
2.69 |
2.2 |

Influence indices
## McFadden's pseudo-R2 is: 0.4
## Tjur's pseudo-R2 is: 0.45
## Pearson's pseudo-R2 is: 0.45
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-0.2322 |
0.6606 |
-0.3514 |
0.7253 |
|
| aquaculture |
2.744 |
3.367 |
0.815 |
0.4151 |
|
| city |
-0.8301 |
4.664 |
-0.178 |
0.8587 |
|
| dredging_collect |
1.24 |
2.238 |
0.5539 |
0.5796 |
|
| dredging_dump |
-3.695 |
2.31 |
-1.6 |
0.1097 |
|
| industry |
1.927 |
1.264 |
1.525 |
0.1274 |
|
| shipping_mooring |
1.902 |
2.59 |
0.7343 |
0.4627 |
|
| shipping_traffic |
2.051 |
1.032 |
1.988 |
0.0468 |
* |
| sewers_rain |
-3.409 |
2.647 |
-1.288 |
0.1978 |
|
| sewers_waste |
4.558 |
3.955 |
1.153 |
0.2491 |
|
| wharves_city |
1.77 |
4.516 |
0.3918 |
0.6952 |
|
| wharves_industry |
-0.9432 |
3.353 |
-0.2813 |
0.7785 |
|
| fisheries_trap |
-0.1131 |
0.3778 |
-0.2994 |
0.7646 |
|
| fisheries_trawl |
-0.1095 |
0.3018 |
-0.3629 |
0.7167 |
|
| fisheries_net |
0.8427 |
3.981 |
0.2117 |
0.8323 |
|
| fisheries_dredge |
2.22 |
1.335 |
1.662 |
0.09643 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11 |
15.6 |
7.63 |
7.98 |
3.16 |
9.01 |
3.23 |
9.04 |
13.6 |
15.8 |
11.3 |
1.07 |
1.43 |
1 |
2.01 |

Eteone sp
## SDM for: eteone_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.19
## Tjur's pseudo-R2 is: 0.1
## Pearson's pseudo-R2 is: 0.1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-369.5 |
57199 |
-0.00646 |
0.9948 |
|
| om |
-2.116 |
1.229 |
-1.722 |
0.08508 |
|
| gravel |
0.4254 |
0.4622 |
0.9204 |
0.3574 |
|
| silt |
0.9025 |
0.9995 |
0.9029 |
0.3666 |
|
| clay |
-1998 |
312123 |
-0.0064 |
0.9949 |
|
| arsenic |
0.3058 |
0.7581 |
0.4034 |
0.6867 |
|
| cadmium |
-0.6232 |
1.017 |
-0.613 |
0.5399 |
|
| copper |
0.8963 |
1.219 |
0.735 |
0.4623 |
|
| iron |
0.2086 |
0.6309 |
0.3307 |
0.7409 |
|
| manganese |
-0.3976 |
1.35 |
-0.2945 |
0.7684 |
|
| mercury |
-0.2233 |
1.253 |
-0.1783 |
0.8585 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.57 |
1.4 |
2.1 |
1 |
1.6 |
1.85 |
2.51 |
1.43 |
2.35 |
1.75 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1108 |
1792834 |
-0.000618 |
0.9995 |
|
| aquaculture |
387.9 |
2955483 |
0.0001313 |
0.9999 |
|
| city |
-1439 |
12678348 |
-0.0001135 |
0.9999 |
|
| dredging_collect |
-2163 |
7965912 |
-0.0002716 |
0.9998 |
|
| dredging_dump |
-1514 |
5613722 |
-0.0002698 |
0.9998 |
|
| industry |
101.7 |
4086051 |
2.488e-05 |
1 |
|
| shipping_mooring |
-305.2 |
6037817 |
-5.055e-05 |
1 |
|
| shipping_traffic |
-96.43 |
2983314 |
-3.232e-05 |
1 |
|
| sewers_rain |
-704.8 |
2660942 |
-0.0002649 |
0.9998 |
|
| sewers_waste |
1386 |
10077376 |
0.0001376 |
0.9999 |
|
| wharves_city |
2337 |
13700047 |
0.0001706 |
0.9999 |
|
| wharves_industry |
2766 |
13868615 |
0.0001994 |
0.9998 |
|
| fisheries_trap |
-9.648 |
285588 |
-3.378e-05 |
1 |
|
| fisheries_trawl |
-5.294 |
584983 |
-9.05e-06 |
1 |
|
| fisheries_net |
-4263 |
6443032 |
-0.0006616 |
0.9995 |
|
| fisheries_dredge |
-402 |
5399278 |
-7.446e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
54.2 |
242 |
158 |
93 |
47.4 |
140 |
28 |
53.6 |
186 |
259 |
254 |
6.01 |
43.4 |
1.58 |
7.02 |

Euchone sp
## SDM for: euchone_sp
Abiotic parameters
## McFadden's pseudo-R2 is: -12.23
## Tjur's pseudo-R2 is: -0.01
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-3.301e+15 |
7978909 |
-413766906 |
0 |
* * * |
| om |
-1.493e+15 |
14257309 |
-104685824 |
0 |
* * * |
| gravel |
7.237e+14 |
8714549 |
83047230 |
0 |
* * * |
| silt |
1.384e+15 |
15274819 |
90612828 |
0 |
* * * |
| clay |
-7.527e+14 |
21858102 |
-34434528 |
0 |
* * * |
| arsenic |
-1.752e+15 |
11951982 |
-146562517 |
0 |
* * * |
| cadmium |
6.267e+14 |
10400421 |
60260861 |
0 |
* * * |
| copper |
1.939e+15 |
13687347 |
141670271 |
0 |
* * * |
| iron |
-4.869e+14 |
9620115 |
-50615564 |
0 |
* * * |
| manganese |
-5.417e+14 |
14288707 |
-37912203 |
0 |
* * * |
| mercury |
-6.123e+14 |
12068606 |
-50738739 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-108.2 |
230852 |
-0.0004686 |
0.9996 |
|
| aquaculture |
-372.7 |
7074900 |
-5.267e-05 |
1 |
|
| city |
-426.4 |
3308637 |
-0.0001289 |
0.9999 |
|
| dredging_collect |
-235.4 |
21696254 |
-1.085e-05 |
1 |
|
| dredging_dump |
-82.52 |
6321537 |
-1.305e-05 |
1 |
|
| industry |
213.3 |
1182221 |
0.0001805 |
0.9999 |
|
| shipping_mooring |
-280.9 |
11462006 |
-2.451e-05 |
1 |
|
| shipping_traffic |
-98.85 |
3244147 |
-3.047e-05 |
1 |
|
| sewers_rain |
103.2 |
6422833 |
1.607e-05 |
1 |
|
| sewers_waste |
-34.09 |
8749498 |
-3.896e-06 |
1 |
|
| wharves_city |
620.2 |
4779428 |
0.0001298 |
0.9999 |
|
| wharves_industry |
91.44 |
28604720 |
3.197e-06 |
1 |
|
| fisheries_trap |
-14.2 |
464038 |
-3.06e-05 |
1 |
|
| fisheries_trawl |
11.79 |
560768 |
2.102e-05 |
1 |
|
| fisheries_net |
5.979 |
272327 |
2.195e-05 |
1 |
|
| fisheries_dredge |
40.26 |
6081530 |
6.621e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
58.6 |
47.9 |
255 |
80.8 |
13 |
103 |
34.1 |
46.3 |
61.4 |
78.8 |
312 |
9.76 |
3.81 |
1.77 |
36.1 |

Eudorella emarginata
## SDM for: eudorella_emarginata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.41
## Tjur's pseudo-R2 is: 0.28
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-258.5 |
60111 |
-0.0043 |
0.9966 |
|
| om |
-0.09617 |
0.9278 |
-0.1037 |
0.9174 |
|
| gravel |
1.322 |
0.8811 |
1.5 |
0.1335 |
|
| silt |
2.358 |
1.743 |
1.353 |
0.1761 |
|
| clay |
-1383 |
328018 |
-0.004215 |
0.9966 |
|
| arsenic |
-0.1808 |
0.7264 |
-0.2488 |
0.8035 |
|
| cadmium |
1.1 |
1.239 |
0.8879 |
0.3746 |
|
| copper |
-0.1378 |
2.327 |
-0.05919 |
0.9528 |
|
| iron |
-0.1505 |
4.708 |
-0.03197 |
0.9745 |
|
| manganese |
-0.0414 |
1.582 |
-0.02617 |
0.9791 |
|
| mercury |
0.6286 |
0.8325 |
0.7551 |
0.4502 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.69 |
2.15 |
2.27 |
1 |
1.48 |
1.92 |
4.71 |
6.57 |
3.16 |
1.66 |

Influence indices
## McFadden's pseudo-R2 is: 0.74
## Tjur's pseudo-R2 is: 0.72
## Pearson's pseudo-R2 is: 0.74
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-143.1 |
1106 |
-0.1294 |
0.8971 |
|
| aquaculture |
-75.89 |
79.81 |
-0.9508 |
0.3417 |
|
| city |
-60.28 |
60.97 |
-0.9887 |
0.3228 |
|
| dredging_collect |
150.8 |
73.68 |
2.047 |
0.04069 |
* |
| dredging_dump |
38.11 |
28.91 |
1.318 |
0.1875 |
|
| industry |
-4.363 |
14.39 |
-0.3031 |
0.7618 |
|
| shipping_mooring |
57.16 |
30.59 |
1.869 |
0.06166 |
|
| shipping_traffic |
16.95 |
28.49 |
0.5949 |
0.5519 |
|
| sewers_rain |
-36.14 |
45.7 |
-0.7908 |
0.429 |
|
| sewers_waste |
116.2 |
80.23 |
1.449 |
0.1474 |
|
| wharves_city |
60.15 |
58.98 |
1.02 |
0.3078 |
|
| wharves_industry |
-236.1 |
122.1 |
-1.934 |
0.05317 |
|
| fisheries_trap |
-7.938 |
8.601 |
-0.9229 |
0.356 |
|
| fisheries_trawl |
45.32 |
2896 |
0.01565 |
0.9875 |
|
| fisheries_net |
16.96 |
7631 |
0.002222 |
0.9982 |
|
| fisheries_dredge |
78.34 |
879.1 |
0.08911 |
0.929 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
23.1 |
39.7 |
46 |
18.4 |
11.3 |
6.68 |
19.5 |
22.2 |
29.9 |
42 |
77.2 |
1.55 |
1 |
1 |
1 |

Eudorellopsis integra
## SDM for: eudorellopsis_integra
Abiotic parameters
## McFadden's pseudo-R2 is: 0.42
## Tjur's pseudo-R2 is: 0.47
## Pearson's pseudo-R2 is: 0.47
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-7.425 |
26.92 |
-0.2758 |
0.7827 |
|
| om |
2.734 |
0.8385 |
3.261 |
0.001111 |
* * |
| gravel |
-0.4487 |
0.4092 |
-1.097 |
0.2728 |
|
| silt |
-0.5124 |
0.5691 |
-0.9003 |
0.3679 |
|
| clay |
-45.45 |
147.3 |
-0.3086 |
0.7577 |
|
| arsenic |
0.6001 |
0.9013 |
0.6659 |
0.5055 |
|
| cadmium |
-0.8911 |
0.444 |
-2.007 |
0.04476 |
* |
| copper |
-1.537 |
0.6382 |
-2.408 |
0.01605 |
* |
| iron |
-1.094 |
0.5621 |
-1.946 |
0.0516 |
|
| manganese |
1.956 |
0.8746 |
2.236 |
0.02534 |
* |
| mercury |
0.5882 |
0.6548 |
0.8983 |
0.369 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.95 |
1.14 |
1.69 |
1.03 |
1.81 |
1.48 |
2.03 |
1.61 |
2.06 |
1.55 |

Influence indices
## McFadden's pseudo-R2 is: 0.84
## Tjur's pseudo-R2 is: 0.86
## Pearson's pseudo-R2 is: 0.87
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-13.43 |
37.49 |
-0.3582 |
0.7202 |
|
| aquaculture |
-8.001 |
11.42 |
-0.7005 |
0.4836 |
|
| city |
-279.4 |
149.3 |
-1.871 |
0.06128 |
|
| dredging_collect |
54.43 |
40.1 |
1.357 |
0.1746 |
|
| dredging_dump |
-33.82 |
26.22 |
-1.29 |
0.1971 |
|
| industry |
21.33 |
12.47 |
1.71 |
0.0872 |
|
| shipping_mooring |
74.41 |
41.56 |
1.79 |
0.07338 |
|
| shipping_traffic |
18.9 |
11.86 |
1.594 |
0.111 |
|
| sewers_rain |
-32.57 |
22.67 |
-1.437 |
0.1508 |
|
| sewers_waste |
-5.669 |
15.37 |
-0.3687 |
0.7123 |
|
| wharves_city |
249.2 |
133.6 |
1.865 |
0.06221 |
|
| wharves_industry |
-59.34 |
44.17 |
-1.344 |
0.1791 |
|
| fisheries_trap |
-1.357 |
3.133 |
-0.433 |
0.665 |
|
| fisheries_trawl |
-1.957 |
2.44 |
-0.8021 |
0.4225 |
|
| fisheries_net |
-1.25 |
380.3 |
-0.003287 |
0.9974 |
|
| fisheries_dredge |
11.26 |
8.824 |
1.276 |
0.202 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
17.5 |
264 |
74.5 |
49.8 |
19.7 |
66.5 |
20.3 |
36.9 |
22.6 |
250 |
79.5 |
2.78 |
4.35 |
1 |
9.42 |

Euspira pallida
## SDM for: euspira_pallida
Abiotic parameters
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.05
## Pearson's pseudo-R2 is: 0.03
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-110.1 |
55630 |
-0.001978 |
0.9984 |
|
| om |
-1.751 |
4.216 |
-0.4154 |
0.6778 |
|
| gravel |
-27.16 |
46141 |
-0.0005886 |
0.9995 |
|
| silt |
1.672 |
3.353 |
0.4987 |
0.618 |
|
| clay |
-521.1 |
295859 |
-0.001761 |
0.9986 |
|
| arsenic |
0.6377 |
3.319 |
0.1921 |
0.8477 |
|
| cadmium |
-1.961 |
3.269 |
-0.5999 |
0.5486 |
|
| copper |
1.817 |
3.22 |
0.5644 |
0.5725 |
|
| iron |
-1.49 |
4.276 |
-0.3486 |
0.7274 |
|
| manganese |
-0.5405 |
8.859 |
-0.06101 |
0.9514 |
|
| mercury |
-2.486 |
4.851 |
-0.5125 |
0.6083 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.99 |
1 |
2.65 |
1 |
1.43 |
1.64 |
1.88 |
2.5 |
2.72 |
2.43 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-270.8 |
653366 |
-0.0004145 |
0.9997 |
|
| aquaculture |
-685.8 |
3347015 |
-0.0002049 |
0.9998 |
|
| city |
-63.64 |
2663918 |
-2.389e-05 |
1 |
|
| dredging_collect |
173.9 |
4035131 |
4.309e-05 |
1 |
|
| dredging_dump |
1255 |
5621187 |
0.0002232 |
0.9998 |
|
| industry |
259.8 |
1740241 |
0.0001493 |
0.9999 |
|
| shipping_mooring |
-561.2 |
6040433 |
-9.291e-05 |
0.9999 |
|
| shipping_traffic |
-122 |
1919311 |
-6.359e-05 |
0.9999 |
|
| sewers_rain |
562.5 |
8546065 |
6.582e-05 |
0.9999 |
|
| sewers_waste |
-724.1 |
11184571 |
-6.474e-05 |
0.9999 |
|
| wharves_city |
-9.422 |
5592519 |
-1.685e-06 |
1 |
|
| wharves_industry |
-1297 |
9453606 |
-0.0001372 |
0.9999 |
|
| fisheries_trap |
-41.32 |
1640425 |
-2.519e-05 |
1 |
|
| fisheries_trawl |
-120.4 |
2698757 |
-4.46e-05 |
1 |
|
| fisheries_net |
38.96 |
748492 |
5.206e-05 |
1 |
|
| fisheries_dredge |
-72.49 |
248939 |
-0.0002912 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
36.4 |
37.4 |
53.5 |
85.6 |
20.2 |
89 |
32.6 |
144 |
175 |
80.4 |
140 |
2.78 |
20.3 |
2.96 |
7.21 |

Glycera capitata
## SDM for: glycera_capitata
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-109.4 |
282149 |
-0.0003877 |
0.9997 |
|
| om |
-74.72 |
3625175 |
-2.061e-05 |
1 |
|
| gravel |
32.36 |
254640 |
0.0001271 |
0.9999 |
|
| silt |
37.08 |
1909038 |
1.942e-05 |
1 |
|
| clay |
13.8 |
1278124 |
1.08e-05 |
1 |
|
| arsenic |
-26.66 |
6007963 |
-4.437e-06 |
1 |
|
| cadmium |
13.47 |
4288346 |
3.142e-06 |
1 |
|
| copper |
47.5 |
2057839 |
2.308e-05 |
1 |
|
| iron |
15.74 |
3857034 |
4.08e-06 |
1 |
|
| manganese |
-23.74 |
2391369 |
-9.928e-06 |
1 |
|
| mercury |
16.82 |
5294634 |
3.177e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
41.3 |
10.8 |
37.4 |
21.4 |
55.5 |
75.2 |
30.9 |
32.5 |
30.9 |
72.2 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-71.64 |
124295 |
-0.0005764 |
0.9995 |
|
| aquaculture |
30.96 |
666058 |
4.648e-05 |
1 |
|
| city |
92.5 |
1376704 |
6.719e-05 |
0.9999 |
|
| dredging_collect |
61.81 |
512461 |
0.0001206 |
0.9999 |
|
| dredging_dump |
-118.9 |
933669 |
-0.0001273 |
0.9999 |
|
| industry |
74.5 |
406565 |
0.0001832 |
0.9999 |
|
| shipping_mooring |
43.49 |
454021 |
9.578e-05 |
0.9999 |
|
| shipping_traffic |
27.12 |
267895 |
0.0001013 |
0.9999 |
|
| sewers_rain |
28.61 |
826541 |
3.462e-05 |
1 |
|
| sewers_waste |
-9.756 |
1423256 |
-6.855e-06 |
1 |
|
| wharves_city |
-118.8 |
1898845 |
-6.256e-05 |
1 |
|
| wharves_industry |
-53.03 |
801086 |
-6.62e-05 |
0.9999 |
|
| fisheries_trap |
0.9245 |
260829 |
3.544e-06 |
1 |
|
| fisheries_trawl |
-6.025 |
45901 |
-0.0001313 |
0.9999 |
|
| fisheries_net |
-0.2387 |
87858 |
-2.717e-06 |
1 |
|
| fisheries_dredge |
-2.371 |
268087 |
-8.844e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.5 |
25.1 |
14 |
20.2 |
9.71 |
9.25 |
5.28 |
21.7 |
28.4 |
34.3 |
20.3 |
2.5 |
2.01 |
1.01 |
1.62 |

Glycera sp
## SDM for: glycera_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.61
## Tjur's pseudo-R2 is: 0.46
## Pearson's pseudo-R2 is: 0.44
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-28.5 |
19.47 |
-1.464 |
0.1433 |
|
| om |
-17 |
12.26 |
-1.387 |
0.1653 |
|
| gravel |
8.449 |
6.468 |
1.306 |
0.1915 |
|
| silt |
10.68 |
7.723 |
1.382 |
0.1669 |
|
| clay |
-3.516 |
11.28 |
-0.3117 |
0.7553 |
|
| arsenic |
-10.01 |
7.086 |
-1.412 |
0.1578 |
|
| cadmium |
14.58 |
10.54 |
1.383 |
0.1667 |
|
| copper |
18.99 |
13.83 |
1.374 |
0.1696 |
|
| iron |
-20.27 |
16.92 |
-1.198 |
0.231 |
|
| manganese |
-2.114 |
3.629 |
-0.5827 |
0.5601 |
|
| mercury |
2.269 |
3.142 |
0.7223 |
0.4701 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.7 |
8.03 |
8.09 |
1.01 |
8.54 |
19.2 |
28.1 |
19.7 |
2.76 |
2.27 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1024 |
2360957 |
-0.0004336 |
0.9997 |
|
| aquaculture |
-100.5 |
2159497 |
-4.653e-05 |
1 |
|
| city |
-128.8 |
3385554 |
-3.804e-05 |
1 |
|
| dredging_collect |
500.3 |
6257509 |
7.995e-05 |
0.9999 |
|
| dredging_dump |
2748 |
7067536 |
0.0003889 |
0.9997 |
|
| industry |
-1009 |
3261005 |
-0.0003093 |
0.9998 |
|
| shipping_mooring |
574.3 |
1819872 |
0.0003156 |
0.9997 |
|
| shipping_traffic |
-1564 |
4039732 |
-0.0003872 |
0.9997 |
|
| sewers_rain |
1821 |
6379893 |
0.0002855 |
0.9998 |
|
| sewers_waste |
-2684 |
10348060 |
-0.0002594 |
0.9998 |
|
| wharves_city |
-907.5 |
4746808 |
-0.0001912 |
0.9998 |
|
| wharves_industry |
-797.1 |
7109287 |
-0.0001121 |
0.9999 |
|
| fisheries_trap |
-207.6 |
929163 |
-0.0002234 |
0.9998 |
|
| fisheries_trawl |
246 |
567331 |
0.0004335 |
0.9997 |
|
| fisheries_net |
120.1 |
6464650 |
1.857e-05 |
1 |
|
| fisheries_dredge |
-381.2 |
5280187 |
-7.22e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
30.5 |
66.6 |
81.2 |
97.6 |
52.2 |
19.4 |
53 |
99.8 |
142 |
81.9 |
92.7 |
1.82 |
2.62 |
1 |
24.1 |

Goniada maculata
## SDM for: goniada_maculata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.23
## Tjur's pseudo-R2 is: 0.25
## Pearson's pseudo-R2 is: 0.25
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-360.8 |
48599 |
-0.007425 |
0.9941 |
|
| om |
0.843 |
0.5315 |
1.586 |
0.1127 |
|
| gravel |
-1.176 |
0.7981 |
-1.473 |
0.1407 |
|
| silt |
-0.06688 |
0.5551 |
-0.1205 |
0.9041 |
|
| clay |
-1966 |
265195 |
-0.007415 |
0.9941 |
|
| arsenic |
0.5867 |
0.4407 |
1.331 |
0.1831 |
|
| cadmium |
-0.1808 |
0.3781 |
-0.4783 |
0.6324 |
|
| copper |
-0.9318 |
0.4928 |
-1.891 |
0.05865 |
|
| iron |
-0.1539 |
0.3115 |
-0.4942 |
0.6212 |
|
| manganese |
-0.5916 |
0.5924 |
-0.9988 |
0.3179 |
|
| mercury |
0.2728 |
0.4396 |
0.6207 |
0.5348 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.01 |
1.1 |
2.11 |
1 |
1.52 |
1.48 |
1.8 |
1.36 |
1.86 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0.47
## Tjur's pseudo-R2 is: 0.5
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-10.38 |
3.603 |
-2.88 |
0.003977 |
* * |
| aquaculture |
3.918 |
3.21 |
1.22 |
0.2223 |
|
| city |
-82.15 |
28.41 |
-2.891 |
0.003836 |
* * |
| dredging_collect |
9.15 |
3.434 |
2.664 |
0.00772 |
* * |
| dredging_dump |
-14.18 |
5.521 |
-2.568 |
0.01022 |
* |
| industry |
-1.904 |
1.411 |
-1.35 |
0.1772 |
|
| shipping_mooring |
31.17 |
10.21 |
3.052 |
0.002271 |
* * |
| shipping_traffic |
4.293 |
1.989 |
2.159 |
0.03087 |
* |
| sewers_rain |
-3.46 |
3.597 |
-0.962 |
0.336 |
|
| sewers_waste |
-19.26 |
8.009 |
-2.405 |
0.01617 |
* |
| wharves_city |
66.8 |
23.39 |
2.856 |
0.004284 |
* * |
| wharves_industry |
7.103 |
4.893 |
1.452 |
0.1466 |
|
| fisheries_trap |
-0.3121 |
0.6213 |
-0.5024 |
0.6154 |
|
| fisheries_trawl |
1.743 |
0.7565 |
2.303 |
0.02126 |
* |
| fisheries_net |
0.9809 |
4.91 |
0.1998 |
0.8417 |
|
| fisheries_dredge |
1.498 |
1.215 |
1.233 |
0.2176 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.1 |
81 |
10.7 |
16.2 |
2.83 |
33.7 |
5.79 |
11.7 |
26.9 |
71.8 |
14.8 |
1.17 |
2.03 |
1 |
2.13 |

Guernea prinassus nordenskioldi
## SDM for: guernea_prinassus_nordenskioldi
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Halacaridae spp
## SDM for: halacaridae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-182.6 |
473133 |
-0.0003859 |
0.9997 |
|
| om |
-134.2 |
358347 |
-0.0003744 |
0.9997 |
|
| gravel |
-4.482 |
286106 |
-1.566e-05 |
1 |
|
| silt |
64.12 |
285563 |
0.0002245 |
0.9998 |
|
| clay |
-105.7 |
1398946 |
-7.555e-05 |
0.9999 |
|
| arsenic |
5.142 |
431968 |
1.19e-05 |
1 |
|
| cadmium |
-40.83 |
219759 |
-0.0001858 |
0.9999 |
|
| copper |
62.63 |
258197 |
0.0002426 |
0.9998 |
|
| iron |
9.686 |
117058 |
8.274e-05 |
0.9999 |
|
| manganese |
-19.25 |
328394 |
-5.861e-05 |
1 |
|
| mercury |
-69.44 |
301137 |
-0.0002306 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.88 |
1.28 |
4.22 |
1.51 |
3.79 |
3.13 |
4.32 |
1.92 |
4.01 |
3.3 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-35.16 |
72323 |
-0.0004862 |
0.9996 |
|
| aquaculture |
-24 |
1358385 |
-1.767e-05 |
1 |
|
| city |
-12.59 |
1297078 |
-9.708e-06 |
1 |
|
| dredging_collect |
-14.92 |
851895 |
-1.751e-05 |
1 |
|
| dredging_dump |
-6.445 |
662645 |
-9.726e-06 |
1 |
|
| industry |
11.9 |
525131 |
2.266e-05 |
1 |
|
| shipping_mooring |
-27.69 |
1438980 |
-1.924e-05 |
1 |
|
| shipping_traffic |
-18.11 |
338086 |
-5.356e-05 |
1 |
|
| sewers_rain |
-19.75 |
1022136 |
-1.932e-05 |
1 |
|
| sewers_waste |
24.54 |
1349796 |
1.818e-05 |
1 |
|
| wharves_city |
28.32 |
1807498 |
1.567e-05 |
1 |
|
| wharves_industry |
31.49 |
777801 |
4.048e-05 |
1 |
|
| fisheries_trap |
-0.6731 |
83030 |
-8.106e-06 |
1 |
|
| fisheries_trawl |
10.88 |
67868 |
0.0001604 |
0.9999 |
|
| fisheries_net |
3.765 |
70648 |
5.329e-05 |
1 |
|
| fisheries_dredge |
0.6407 |
190947 |
3.355e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
27.5 |
18.2 |
14.2 |
11 |
9.45 |
23.7 |
5.79 |
15.1 |
21.9 |
25.4 |
12.8 |
1.63 |
4.13 |
1.25 |
2.35 |

Haminoea solitaria
## SDM for: haminoea_solitaria
Abiotic parameters
## McFadden's pseudo-R2 is: 0.4
## Tjur's pseudo-R2 is: 0.09
## Pearson's pseudo-R2 is: 0.06
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-28.33 |
7334 |
-0.003863 |
0.9969 |
|
| om |
2.883 |
8.032 |
0.3589 |
0.7196 |
|
| gravel |
-26.04 |
11468 |
-0.002271 |
0.9982 |
|
| silt |
-1.792 |
4.549 |
-0.3939 |
0.6936 |
|
| clay |
-48.61 |
35981 |
-0.001351 |
0.9989 |
|
| arsenic |
-1.186 |
25.9 |
-0.04579 |
0.9635 |
|
| cadmium |
1.978 |
4.607 |
0.4293 |
0.6677 |
|
| copper |
-0.6456 |
6.62 |
-0.09751 |
0.9223 |
|
| iron |
-2.897 |
11.07 |
-0.2618 |
0.7935 |
|
| manganese |
-4.971 |
18.11 |
-0.2745 |
0.7837 |
|
| mercury |
-3.972 |
8.918 |
-0.4454 |
0.656 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
6.05 |
1 |
4.36 |
1 |
4.03 |
3.02 |
2.08 |
2.48 |
2.39 |
3.25 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-140.7 |
328669 |
-0.0004281 |
0.9997 |
|
| aquaculture |
229.6 |
10937180 |
2.1e-05 |
1 |
|
| city |
273.9 |
8728790 |
3.137e-05 |
1 |
|
| dredging_collect |
-365.4 |
6986522 |
-5.23e-05 |
1 |
|
| dredging_dump |
-153.6 |
3458238 |
-4.441e-05 |
1 |
|
| industry |
-59.61 |
4474646 |
-1.332e-05 |
1 |
|
| shipping_mooring |
167.9 |
8742986 |
1.92e-05 |
1 |
|
| shipping_traffic |
17.97 |
1883919 |
9.537e-06 |
1 |
|
| sewers_rain |
-66.53 |
2693857 |
-2.47e-05 |
1 |
|
| sewers_waste |
177 |
3450483 |
5.128e-05 |
1 |
|
| wharves_city |
-232.7 |
12961639 |
-1.795e-05 |
1 |
|
| wharves_industry |
536.6 |
5483854 |
9.786e-05 |
0.9999 |
|
| fisheries_trap |
-17.42 |
157690 |
-0.0001105 |
0.9999 |
|
| fisheries_trawl |
-21.83 |
446444 |
-4.889e-05 |
1 |
|
| fisheries_net |
11.02 |
206739 |
5.329e-05 |
1 |
|
| fisheries_dredge |
204.7 |
653920 |
0.0003131 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
197 |
115 |
110 |
48.9 |
47 |
149 |
30.4 |
48.4 |
60.6 |
161 |
85.6 |
2.68 |
4.32 |
1.35 |
9.3 |

Hardametopa carinata
## SDM for: hardametopa_carinata
Abiotic parameters
## McFadden's pseudo-R2 is: -12.23
## Tjur's pseudo-R2 is: -0.01
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-3.301e+15 |
7978909 |
-413766906 |
0 |
* * * |
| om |
-1.493e+15 |
14257309 |
-104685824 |
0 |
* * * |
| gravel |
7.237e+14 |
8714549 |
83047230 |
0 |
* * * |
| silt |
1.384e+15 |
15274819 |
90612828 |
0 |
* * * |
| clay |
-7.527e+14 |
21858102 |
-34434528 |
0 |
* * * |
| arsenic |
-1.752e+15 |
11951982 |
-146562517 |
0 |
* * * |
| cadmium |
6.267e+14 |
10400421 |
60260861 |
0 |
* * * |
| copper |
1.939e+15 |
13687347 |
141670271 |
0 |
* * * |
| iron |
-4.869e+14 |
9620115 |
-50615564 |
0 |
* * * |
| manganese |
-5.417e+14 |
14288707 |
-37912203 |
0 |
* * * |
| mercury |
-6.123e+14 |
12068606 |
-50738739 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-108.2 |
230852 |
-0.0004686 |
0.9996 |
|
| aquaculture |
-372.7 |
7074900 |
-5.267e-05 |
1 |
|
| city |
-426.4 |
3308637 |
-0.0001289 |
0.9999 |
|
| dredging_collect |
-235.4 |
21696254 |
-1.085e-05 |
1 |
|
| dredging_dump |
-82.52 |
6321537 |
-1.305e-05 |
1 |
|
| industry |
213.3 |
1182221 |
0.0001805 |
0.9999 |
|
| shipping_mooring |
-280.9 |
11462006 |
-2.451e-05 |
1 |
|
| shipping_traffic |
-98.85 |
3244147 |
-3.047e-05 |
1 |
|
| sewers_rain |
103.2 |
6422833 |
1.607e-05 |
1 |
|
| sewers_waste |
-34.09 |
8749498 |
-3.896e-06 |
1 |
|
| wharves_city |
620.2 |
4779428 |
0.0001298 |
0.9999 |
|
| wharves_industry |
91.44 |
28604720 |
3.197e-06 |
1 |
|
| fisheries_trap |
-14.2 |
464038 |
-3.06e-05 |
1 |
|
| fisheries_trawl |
11.79 |
560768 |
2.102e-05 |
1 |
|
| fisheries_net |
5.979 |
272327 |
2.195e-05 |
1 |
|
| fisheries_dredge |
40.26 |
6081530 |
6.621e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
58.6 |
47.9 |
255 |
80.8 |
13 |
103 |
34.1 |
46.3 |
61.4 |
78.8 |
312 |
9.76 |
3.81 |
1.77 |
36.1 |

Harmothoe sp
## SDM for: harmothoe_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.31
## Tjur's pseudo-R2 is: 0.15
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-297.4 |
123467 |
-0.002409 |
0.9981 |
|
| om |
-1.349 |
1.648 |
-0.8187 |
0.413 |
|
| gravel |
1.754 |
1.197 |
1.465 |
0.1428 |
|
| silt |
3.209 |
3.034 |
1.058 |
0.2901 |
|
| clay |
-1593 |
673737 |
-0.002365 |
0.9981 |
|
| arsenic |
-0.8131 |
1.986 |
-0.4093 |
0.6823 |
|
| cadmium |
-0.8947 |
2.331 |
-0.3838 |
0.7011 |
|
| copper |
1.42 |
2.483 |
0.5717 |
0.5675 |
|
| iron |
0.08593 |
1.526 |
0.0563 |
0.9551 |
|
| manganese |
1.257 |
2.861 |
0.4393 |
0.6605 |
|
| mercury |
-2.617 |
2.43 |
-1.077 |
0.2815 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.2 |
3.61 |
4.59 |
1 |
1.72 |
2.43 |
3.03 |
1.68 |
3.59 |
2.85 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-67.01 |
140671 |
-0.0004764 |
0.9996 |
|
| aquaculture |
71.4 |
4855881 |
1.47e-05 |
1 |
|
| city |
145.5 |
10430881 |
1.395e-05 |
1 |
|
| dredging_collect |
77.28 |
774915 |
9.973e-05 |
0.9999 |
|
| dredging_dump |
-69.1 |
2160445 |
-3.199e-05 |
1 |
|
| industry |
46.68 |
2184647 |
2.137e-05 |
1 |
|
| shipping_mooring |
71.69 |
2732105 |
2.624e-05 |
1 |
|
| shipping_traffic |
44.99 |
2913228 |
1.544e-05 |
1 |
|
| sewers_rain |
-7.218 |
4567222 |
-1.58e-06 |
1 |
|
| sewers_waste |
13.37 |
2628820 |
5.086e-06 |
1 |
|
| wharves_city |
-175.3 |
10730018 |
-1.633e-05 |
1 |
|
| wharves_industry |
-93.08 |
1858782 |
-5.008e-05 |
1 |
|
| fisheries_trap |
6.762 |
579912 |
1.166e-05 |
1 |
|
| fisheries_trawl |
-4.493 |
150652 |
-2.982e-05 |
1 |
|
| fisheries_net |
8.237 |
405683 |
2.03e-05 |
1 |
|
| fisheries_dredge |
-8.058 |
914003 |
-8.816e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
78.4 |
177 |
16.8 |
41 |
44.1 |
52.6 |
45.5 |
68 |
41.8 |
171 |
36.4 |
8.2 |
4.69 |
4.34 |
5.39 |

Harpacticoida
## SDM for: harpacticoida
Abiotic parameters
## McFadden's pseudo-R2 is: 0.15
## Tjur's pseudo-R2 is: 0.18
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-0.1307 |
0.279 |
-0.4684 |
0.6395 |
|
| om |
-0.2423 |
0.4816 |
-0.5032 |
0.6148 |
|
| gravel |
-0.4946 |
0.3036 |
-1.629 |
0.1033 |
|
| silt |
-0.9537 |
0.5323 |
-1.792 |
0.07321 |
|
| clay |
0.01964 |
0.6813 |
0.02883 |
0.977 |
|
| arsenic |
-0.6126 |
0.6537 |
-0.9372 |
0.3487 |
|
| cadmium |
0.09313 |
0.3354 |
0.2777 |
0.7813 |
|
| copper |
0.2464 |
0.4647 |
0.5302 |
0.5959 |
|
| iron |
-0.007579 |
0.2988 |
-0.02537 |
0.9798 |
|
| manganese |
-0.2256 |
0.483 |
-0.467 |
0.6405 |
|
| mercury |
0.9802 |
0.4596 |
2.133 |
0.03295 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.86 |
1.39 |
2.13 |
1.1 |
1.83 |
1.45 |
1.93 |
1.37 |
1.81 |
1.72 |

Influence indices
## McFadden's pseudo-R2 is: 0.33
## Tjur's pseudo-R2 is: 0.39
## Pearson's pseudo-R2 is: 0.38
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-60.41 |
7297 |
-0.008279 |
0.9934 |
|
| aquaculture |
-2.306 |
2.527 |
-0.9127 |
0.3614 |
|
| city |
-1.173 |
2.369 |
-0.4949 |
0.6206 |
|
| dredging_collect |
5.026 |
2.276 |
2.208 |
0.02723 |
* |
| dredging_dump |
-0.5171 |
2.027 |
-0.2551 |
0.7987 |
|
| industry |
0.4183 |
1.09 |
0.3838 |
0.7012 |
|
| shipping_mooring |
-0.4469 |
1.866 |
-0.2394 |
0.8108 |
|
| shipping_traffic |
1.482 |
0.9529 |
1.555 |
0.1199 |
|
| sewers_rain |
-1.321 |
2.45 |
-0.5392 |
0.5897 |
|
| sewers_waste |
0.6535 |
3.281 |
0.1992 |
0.8421 |
|
| wharves_city |
2.4 |
2.821 |
0.8509 |
0.3948 |
|
| wharves_industry |
-6.804 |
3.41 |
-1.995 |
0.04602 |
* |
| fisheries_trap |
0.1792 |
0.4054 |
0.4421 |
0.6585 |
|
| fisheries_trawl |
0.0565 |
0.3018 |
0.1872 |
0.8515 |
|
| fisheries_net |
-625.7 |
75659 |
-0.008271 |
0.9934 |
|
| fisheries_dredge |
-0.06066 |
1.075 |
-0.05643 |
0.955 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
8.6 |
8.78 |
7.88 |
7.39 |
4.23 |
5.88 |
3.44 |
6.93 |
9.95 |
10.6 |
12.1 |
1.05 |
1.39 |
1 |
1.44 |

Hediste diversicolor
## SDM for: hediste_diversicolor
Abiotic parameters
## McFadden's pseudo-R2 is: 0.41
## Tjur's pseudo-R2 is: 0.38
## Pearson's pseudo-R2 is: 0.37
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-4.26 |
1.173 |
-3.632 |
0.0002807 |
* * * |
| om |
0.5063 |
0.8401 |
0.6027 |
0.5467 |
|
| gravel |
-0.2704 |
0.5688 |
-0.4753 |
0.6346 |
|
| silt |
-0.1369 |
0.9696 |
-0.1412 |
0.8877 |
|
| clay |
1.074 |
0.8815 |
1.218 |
0.2232 |
|
| arsenic |
-6.008 |
2.312 |
-2.598 |
0.009373 |
* * |
| cadmium |
1.08 |
0.6571 |
1.643 |
0.1004 |
|
| copper |
2.731 |
1.241 |
2.2 |
0.02783 |
* |
| iron |
-5.956 |
2.654 |
-2.245 |
0.0248 |
* |
| manganese |
4.928 |
2.201 |
2.239 |
0.02515 |
* |
| mercury |
-1.069 |
1.147 |
-0.9322 |
0.3512 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.24 |
1.81 |
2.25 |
1.36 |
2.7 |
1.4 |
2.69 |
5.01 |
5.91 |
2.62 |

Influence indices
## McFadden's pseudo-R2 is: -9.37
## Tjur's pseudo-R2 is: 0.52
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.496e+15 |
7496963 |
-199610397 |
0 |
* * * |
| aquaculture |
-2.707e+14 |
66771996 |
-4053672 |
0 |
* * * |
| city |
6.524e+14 |
60515998 |
10779871 |
0 |
* * * |
| dredging_collect |
2.138e+15 |
47886179 |
44656506 |
0 |
* * * |
| dredging_dump |
2.622e+15 |
55589796 |
47167271 |
0 |
* * * |
| industry |
-6.664e+14 |
30193191 |
-22072515 |
0 |
* * * |
| shipping_mooring |
-2.245e+14 |
51224704 |
-4383618 |
0 |
* * * |
| shipping_traffic |
-5.961e+14 |
22815885 |
-26125201 |
0 |
* * * |
| sewers_rain |
1.942e+15 |
67164046 |
28908808 |
0 |
* * * |
| sewers_waste |
-1.699e+15 |
90359677 |
-18805353 |
0 |
* * * |
| wharves_city |
-1.222e+15 |
72465526 |
-16857801 |
0 |
* * * |
| wharves_industry |
-3.239e+15 |
79067645 |
-40960911 |
0 |
* * * |
| fisheries_trap |
-3.874e+13 |
7277539 |
-5323487 |
0 |
* * * |
| fisheries_trawl |
-9.891e+13 |
8821984 |
-11211621 |
0 |
* * * |
| fisheries_net |
-3.76e+14 |
7219163 |
-52088433 |
0 |
* * * |
| fisheries_dredge |
-6.069e+14 |
19454028 |
-31194212 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Heteranomia squamula
## SDM for: heteranomia_squamula
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-95.8 |
274909 |
-0.0003485 |
0.9997 |
|
| om |
-10.42 |
524836 |
-1.985e-05 |
1 |
|
| gravel |
6.602 |
77488 |
8.52e-05 |
0.9999 |
|
| silt |
-1.753 |
229514 |
-7.637e-06 |
1 |
|
| clay |
23.84 |
1153878 |
2.066e-05 |
1 |
|
| arsenic |
-107.1 |
345443 |
-0.0003101 |
0.9998 |
|
| cadmium |
-11.83 |
187356 |
-6.316e-05 |
0.9999 |
|
| copper |
7.065 |
286431 |
2.467e-05 |
1 |
|
| iron |
-26.82 |
273093 |
-9.822e-05 |
0.9999 |
|
| manganese |
42.82 |
545233 |
7.854e-05 |
0.9999 |
|
| mercury |
10.22 |
290476 |
3.52e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.62 |
2.62 |
3.86 |
2 |
1.76 |
5.63 |
3.51 |
3.92 |
8.2 |
3.55 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-116.6 |
240165 |
-0.0004857 |
0.9996 |
|
| aquaculture |
-168.3 |
1102516 |
-0.0001526 |
0.9999 |
|
| city |
251.4 |
1289120 |
0.000195 |
0.9998 |
|
| dredging_collect |
9.64 |
1661898 |
5.801e-06 |
1 |
|
| dredging_dump |
151.3 |
1022875 |
0.000148 |
0.9999 |
|
| industry |
-34.59 |
566055 |
-6.111e-05 |
1 |
|
| shipping_mooring |
2.434 |
869643 |
2.799e-06 |
1 |
|
| shipping_traffic |
-48.51 |
852461 |
-5.69e-05 |
1 |
|
| sewers_rain |
332.8 |
1136950 |
0.0002927 |
0.9998 |
|
| sewers_waste |
-562 |
1673553 |
-0.0003358 |
0.9997 |
|
| wharves_city |
-312.7 |
1361612 |
-0.0002297 |
0.9998 |
|
| wharves_industry |
27.32 |
2040076 |
1.339e-05 |
1 |
|
| fisheries_trap |
18.7 |
82254 |
0.0002273 |
0.9998 |
|
| fisheries_trawl |
16.11 |
167002 |
9.649e-05 |
0.9999 |
|
| fisheries_net |
3.69 |
162216 |
2.275e-05 |
1 |
|
| fisheries_dredge |
30.81 |
692729 |
4.448e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
12.6 |
20.1 |
19 |
12.9 |
6.88 |
7.74 |
11 |
11.3 |
15.7 |
23.9 |
24.1 |
2.03 |
2 |
1.04 |
6.34 |

Hiatella arctica
## SDM for: hiatella_arctica
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2069 |
2995166 |
-0.0006908 |
0.9994 |
|
| om |
-880.4 |
1369615 |
-0.0006428 |
0.9995 |
|
| gravel |
66.57 |
119194 |
0.0005585 |
0.9996 |
|
| silt |
222.7 |
384679 |
0.000579 |
0.9995 |
|
| clay |
410.3 |
622924 |
0.0006587 |
0.9995 |
|
| arsenic |
-1433 |
2195989 |
-0.0006526 |
0.9995 |
|
| cadmium |
-57.33 |
194404 |
-0.0002949 |
0.9998 |
|
| copper |
318.8 |
534368 |
0.0005965 |
0.9995 |
|
| iron |
113.7 |
342324 |
0.0003321 |
0.9997 |
|
| manganese |
-701.2 |
1328934 |
-0.0005276 |
0.9996 |
|
| mercury |
-355.2 |
613684 |
-0.0005788 |
0.9995 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.3 |
4.01 |
8.62 |
5.51 |
7.55 |
2.85 |
6.95 |
4.61 |
6.53 |
6.88 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-443.8 |
1029448 |
-0.0004311 |
0.9997 |
|
| aquaculture |
-600.8 |
2609621 |
-0.0002302 |
0.9998 |
|
| city |
812.1 |
2162626 |
0.0003755 |
0.9997 |
|
| dredging_collect |
-82.7 |
4328889 |
-1.91e-05 |
1 |
|
| dredging_dump |
793.4 |
2288047 |
0.0003468 |
0.9997 |
|
| industry |
-281.4 |
1022890 |
-0.0002751 |
0.9998 |
|
| shipping_mooring |
-67.99 |
799813 |
-8.501e-05 |
0.9999 |
|
| shipping_traffic |
-94.08 |
612474 |
-0.0001536 |
0.9999 |
|
| sewers_rain |
1398 |
3391622 |
0.0004122 |
0.9997 |
|
| sewers_waste |
-2405 |
5935618 |
-0.0004052 |
0.9997 |
|
| wharves_city |
-1166 |
3147721 |
-0.0003703 |
0.9997 |
|
| wharves_industry |
-4.435 |
4562613 |
-9.72e-07 |
1 |
|
| fisheries_trap |
12.56 |
204042 |
6.154e-05 |
1 |
|
| fisheries_trawl |
-248.2 |
1438760 |
-0.0001725 |
0.9999 |
|
| fisheries_net |
-6.259 |
6453060 |
-9.7e-07 |
1 |
|
| fisheries_dredge |
-79.1 |
1875302 |
-4.218e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
58.2 |
33.1 |
66.9 |
36.5 |
14.6 |
15.4 |
13.2 |
67.5 |
100 |
50.5 |
74.1 |
2.22 |
1.61 |
1.02 |
17.6 |

Holothuroidea
## SDM for: holothuroidea
Abiotic parameters
## McFadden's pseudo-R2 is: 0.65
## Tjur's pseudo-R2 is: 0.52
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-41.99 |
38706 |
-0.001085 |
0.9991 |
|
| om |
2.056 |
4.859 |
0.4231 |
0.6722 |
|
| gravel |
-30.85 |
29306 |
-0.001053 |
0.9992 |
|
| silt |
-4.327 |
4.403 |
-0.9826 |
0.3258 |
|
| clay |
-135.6 |
206676 |
-0.0006559 |
0.9995 |
|
| arsenic |
-1.495 |
9.051 |
-0.1652 |
0.8688 |
|
| cadmium |
-1.857 |
3.493 |
-0.5317 |
0.5949 |
|
| copper |
3.585 |
3.232 |
1.109 |
0.2674 |
|
| iron |
-1.275 |
2.689 |
-0.4743 |
0.6353 |
|
| manganese |
-2.835 |
7.675 |
-0.3694 |
0.7119 |
|
| mercury |
-0.6491 |
4.929 |
-0.1317 |
0.8952 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.41 |
1 |
2.44 |
1 |
1.83 |
2.46 |
2.64 |
2.12 |
2.41 |
2.37 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-117.5 |
231099 |
-0.0005084 |
0.9996 |
|
| aquaculture |
-109.7 |
2079988 |
-5.276e-05 |
1 |
|
| city |
176 |
1291820 |
0.0001362 |
0.9999 |
|
| dredging_collect |
157.6 |
914144 |
0.0001724 |
0.9999 |
|
| dredging_dump |
-245.8 |
1839023 |
-0.0001337 |
0.9999 |
|
| industry |
37.95 |
852624 |
4.451e-05 |
1 |
|
| shipping_mooring |
-3.61 |
1441338 |
-2.504e-06 |
1 |
|
| shipping_traffic |
129 |
634858 |
0.0002032 |
0.9998 |
|
| sewers_rain |
-84.31 |
1654545 |
-5.096e-05 |
1 |
|
| sewers_waste |
26 |
2349337 |
1.107e-05 |
1 |
|
| wharves_city |
-95.41 |
1549425 |
-6.157e-05 |
1 |
|
| wharves_industry |
-4.361 |
1951443 |
-2.235e-06 |
1 |
|
| fisheries_trap |
14.51 |
160817 |
9.021e-05 |
0.9999 |
|
| fisheries_trawl |
21.86 |
161670 |
0.0001352 |
0.9999 |
|
| fisheries_net |
11.04 |
167222 |
6.6e-05 |
0.9999 |
|
| fisheries_dredge |
49.21 |
159347 |
0.0003088 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
22.4 |
23.1 |
11.3 |
32.8 |
10.1 |
22.4 |
13.7 |
30.3 |
33.6 |
28.8 |
35.8 |
3.03 |
1.87 |
1.08 |
4.13 |

Idotea phosphorea
## SDM for: idotea_phosphorea
Abiotic parameters
## McFadden's pseudo-R2 is: 0.42
## Tjur's pseudo-R2 is: 0.23
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-10.73 |
5.744 |
-1.867 |
0.06186 |
|
| om |
-4.45 |
2.928 |
-1.52 |
0.1285 |
|
| gravel |
1.461 |
1.319 |
1.107 |
0.2681 |
|
| silt |
2.092 |
1.525 |
1.372 |
0.1701 |
|
| clay |
-2.414 |
7.27 |
-0.332 |
0.7399 |
|
| arsenic |
-0.5591 |
4.273 |
-0.1308 |
0.8959 |
|
| cadmium |
4.091 |
2.911 |
1.406 |
0.1599 |
|
| copper |
5.016 |
3.68 |
1.363 |
0.1729 |
|
| iron |
-7.252 |
6.092 |
-1.19 |
0.2339 |
|
| manganese |
-3.492 |
5.555 |
-0.6286 |
0.5296 |
|
| mercury |
-2.26 |
2.601 |
-0.8692 |
0.3847 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.12 |
1.91 |
2.06 |
1 |
2.46 |
3.82 |
3.43 |
4.39 |
3.15 |
1.18 |

Influence indices
## McFadden's pseudo-R2 is: -12.85
## Tjur's pseudo-R2 is: -0.02
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.572e+15 |
7496963 |
-3.43e+08 |
0 |
* * * |
| aquaculture |
1.796e+15 |
66771996 |
26895007 |
0 |
* * * |
| city |
-4.621e+13 |
60515998 |
-763659 |
0 |
* * * |
| dredging_collect |
1.622e+15 |
47886179 |
33882278 |
0 |
* * * |
| dredging_dump |
3.937e+15 |
55589796 |
70828526 |
0 |
* * * |
| industry |
-2.988e+15 |
30193191 |
-98976231 |
0 |
* * * |
| shipping_mooring |
1.311e+15 |
51224704 |
25595241 |
0 |
* * * |
| shipping_traffic |
3.257e+14 |
22815885 |
14276787 |
0 |
* * * |
| sewers_rain |
2.636e+15 |
67164046 |
39249060 |
0 |
* * * |
| sewers_waste |
-3.848e+15 |
90359677 |
-42583707 |
0 |
* * * |
| wharves_city |
-1.15e+15 |
72465526 |
-15874733 |
0 |
* * * |
| wharves_industry |
-3.483e+15 |
79067645 |
-44050203 |
0 |
* * * |
| fisheries_trap |
-1.856e+14 |
7277539 |
-25500734 |
0 |
* * * |
| fisheries_trawl |
-6.092e+14 |
8821984 |
-69059358 |
0 |
* * * |
| fisheries_net |
-4.158e+14 |
7219163 |
-57597454 |
0 |
* * * |
| fisheries_dredge |
-5.693e+14 |
19454028 |
-29265673 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Ischyroceridae spp
## SDM for: ischyroceridae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-164.2 |
390466 |
-0.0004205 |
0.9997 |
|
| om |
123.5 |
326201 |
0.0003785 |
0.9997 |
|
| gravel |
-37.77 |
359356 |
-0.0001051 |
0.9999 |
|
| silt |
-77.04 |
770127 |
-1e-04 |
0.9999 |
|
| clay |
98.65 |
686832 |
0.0001436 |
0.9999 |
|
| arsenic |
-142.2 |
711751 |
-0.0001998 |
0.9998 |
|
| cadmium |
-27.17 |
605434 |
-4.488e-05 |
1 |
|
| copper |
97.63 |
388985 |
0.000251 |
0.9998 |
|
| iron |
-116.7 |
737376 |
-0.0001583 |
0.9999 |
|
| manganese |
37.35 |
552277 |
6.762e-05 |
0.9999 |
|
| mercury |
30.18 |
251967 |
0.0001198 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.82 |
2.89 |
9.81 |
1.1 |
5.57 |
9.06 |
5.56 |
8.88 |
6.16 |
3.33 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-90.81 |
346796 |
-0.0002618 |
0.9998 |
|
| aquaculture |
337.7 |
14867673 |
2.271e-05 |
1 |
|
| city |
74.87 |
20706774 |
3.616e-06 |
1 |
|
| dredging_collect |
226.6 |
6417189 |
3.531e-05 |
1 |
|
| dredging_dump |
56.87 |
5003265 |
1.137e-05 |
1 |
|
| industry |
-175.2 |
1904624 |
-9.2e-05 |
0.9999 |
|
| shipping_mooring |
293.7 |
8995786 |
3.265e-05 |
1 |
|
| shipping_traffic |
66.97 |
5979316 |
1.12e-05 |
1 |
|
| sewers_rain |
-15.88 |
2149956 |
-7.387e-06 |
1 |
|
| sewers_waste |
25.65 |
7702137 |
3.331e-06 |
1 |
|
| wharves_city |
-192.5 |
20119042 |
-9.568e-06 |
1 |
|
| wharves_industry |
-235.9 |
2676196 |
-8.815e-05 |
0.9999 |
|
| fisheries_trap |
-7.447 |
345469 |
-2.156e-05 |
1 |
|
| fisheries_trawl |
-15.2 |
1249359 |
-1.216e-05 |
1 |
|
| fisheries_net |
-3.215 |
223978 |
-1.435e-05 |
1 |
|
| fisheries_dredge |
1.873 |
1597431 |
1.173e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
125 |
305 |
57.9 |
55.2 |
10.8 |
85.1 |
55.5 |
18.4 |
63.7 |
330 |
24.6 |
2.21 |
12.2 |
1.46 |
9.59 |

Ischyrocerus anguipes
## SDM for: ischyrocerus_anguipes
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-319.1 |
634744 |
-0.0005027 |
0.9996 |
|
| om |
144.7 |
353727 |
0.0004092 |
0.9997 |
|
| gravel |
-32.85 |
217729 |
-0.0001509 |
0.9999 |
|
| silt |
-158.5 |
343731 |
-0.0004611 |
0.9996 |
|
| clay |
125.3 |
1084890 |
0.0001155 |
0.9999 |
|
| arsenic |
-184.5 |
436052 |
-0.0004231 |
0.9997 |
|
| cadmium |
223.3 |
455413 |
0.0004903 |
0.9996 |
|
| copper |
273.8 |
533741 |
0.0005131 |
0.9996 |
|
| iron |
-342.8 |
882909 |
-0.0003883 |
0.9997 |
|
| manganese |
38.51 |
562287 |
6.849e-05 |
0.9999 |
|
| mercury |
-6.083 |
134269 |
-4.531e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.27 |
2.29 |
3.06 |
1.03 |
2.44 |
6.49 |
7.75 |
5.88 |
4.12 |
1.71 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-168.3 |
395546 |
-0.0004254 |
0.9997 |
|
| aquaculture |
-234.5 |
1467949 |
-0.0001597 |
0.9999 |
|
| city |
-220.3 |
1125202 |
-0.0001958 |
0.9998 |
|
| dredging_collect |
-144.8 |
1151325 |
-0.0001257 |
0.9999 |
|
| dredging_dump |
53.78 |
1473918 |
3.649e-05 |
1 |
|
| industry |
20.69 |
740181 |
2.796e-05 |
1 |
|
| shipping_mooring |
-276.2 |
947571 |
-0.0002915 |
0.9998 |
|
| shipping_traffic |
-75.77 |
3504485 |
-2.162e-05 |
1 |
|
| sewers_rain |
-170.3 |
2071718 |
-8.22e-05 |
0.9999 |
|
| sewers_waste |
148.1 |
2867421 |
5.166e-05 |
1 |
|
| wharves_city |
347.5 |
1350164 |
0.0002574 |
0.9998 |
|
| wharves_industry |
122.7 |
4656250 |
2.635e-05 |
1 |
|
| fisheries_trap |
-212.6 |
608992 |
-0.0003491 |
0.9997 |
|
| fisheries_trawl |
18.76 |
4028777 |
4.655e-06 |
1 |
|
| fisheries_net |
32.87 |
477089 |
6.891e-05 |
0.9999 |
|
| fisheries_dredge |
-27.6 |
456481 |
-6.046e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
12.8 |
15 |
17 |
25.2 |
11.5 |
9.14 |
54.1 |
17.4 |
22.8 |
22.2 |
69.1 |
1.7 |
28.3 |
1.89 |
1.85 |

Isopoda
## SDM for: isopoda
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Lacuna vincta
## SDM for: lacuna_vincta
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-511.8 |
862740 |
-0.0005932 |
0.9995 |
|
| om |
-261.7 |
454872 |
-0.0005754 |
0.9995 |
|
| gravel |
86.71 |
157831 |
0.0005494 |
0.9996 |
|
| silt |
75.79 |
203871 |
0.0003718 |
0.9997 |
|
| clay |
43.83 |
271758 |
0.0001613 |
0.9999 |
|
| arsenic |
54.42 |
2718342 |
2.002e-05 |
1 |
|
| cadmium |
229.9 |
761955 |
0.0003017 |
0.9998 |
|
| copper |
104.5 |
342807 |
0.0003048 |
0.9998 |
|
| iron |
-256.6 |
4070557 |
-6.304e-05 |
0.9999 |
|
| manganese |
-191.8 |
3350724 |
-5.723e-05 |
1 |
|
| mercury |
-14.6 |
1024508 |
-1.425e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.79 |
3.93 |
3.6 |
1.1 |
13.2 |
11 |
4.14 |
31.3 |
26.9 |
6.64 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-239.4 |
988003 |
-0.0002423 |
0.9998 |
|
| aquaculture |
1113 |
17795634 |
6.255e-05 |
1 |
|
| city |
385.5 |
2236045 |
0.0001724 |
0.9999 |
|
| dredging_collect |
133.7 |
12374406 |
1.08e-05 |
1 |
|
| dredging_dump |
-81.54 |
3307523 |
-2.465e-05 |
1 |
|
| industry |
-464.9 |
2014431 |
-0.0002308 |
0.9998 |
|
| shipping_mooring |
701.1 |
3892682 |
0.0001801 |
0.9999 |
|
| shipping_traffic |
186.5 |
2400153 |
7.772e-05 |
0.9999 |
|
| sewers_rain |
-409.1 |
16316413 |
-2.507e-05 |
1 |
|
| sewers_waste |
636.8 |
21629851 |
2.944e-05 |
1 |
|
| wharves_city |
-596.9 |
3843564 |
-0.0001553 |
0.9999 |
|
| wharves_industry |
15.41 |
12632497 |
1.22e-06 |
1 |
|
| fisheries_trap |
32.12 |
262169 |
0.0001225 |
0.9999 |
|
| fisheries_trawl |
-42.42 |
1651366 |
-2.569e-05 |
1 |
|
| fisheries_net |
4.778 |
578096 |
8.264e-06 |
1 |
|
| fisheries_dredge |
-125.8 |
15207593 |
-8.275e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
231 |
55.9 |
212 |
39.3 |
35.2 |
45.5 |
49.4 |
181 |
185 |
69.6 |
223 |
7.63 |
9.47 |
2.29 |
79.5 |

Lamprops fuscatus
## SDM for: lamprops_fuscatus
Abiotic parameters
## McFadden's pseudo-R2 is: 0.19
## Tjur's pseudo-R2 is: 0.16
## Pearson's pseudo-R2 is: 0.15
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.959 |
0.7149 |
-4.138 |
3.498e-05 |
* * * |
| om |
-0.988 |
0.8626 |
-1.145 |
0.2521 |
|
| gravel |
-0.5766 |
0.8832 |
-0.6528 |
0.5139 |
|
| silt |
0.159 |
0.7361 |
0.2159 |
0.829 |
|
| clay |
-0.5512 |
1.471 |
-0.3747 |
0.7079 |
|
| arsenic |
-2.131 |
1.523 |
-1.399 |
0.1617 |
|
| cadmium |
-0.3191 |
0.5505 |
-0.5796 |
0.5622 |
|
| copper |
0.6262 |
0.6349 |
0.9863 |
0.324 |
|
| iron |
0.4182 |
0.4128 |
1.013 |
0.3111 |
|
| manganese |
0.3923 |
0.9809 |
0.3999 |
0.6892 |
|
| mercury |
0.04733 |
0.7228 |
0.06548 |
0.9478 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.52 |
1.12 |
1.66 |
1.05 |
1.6 |
1.42 |
1.48 |
1.46 |
1.99 |
1.58 |

Influence indices
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.29
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-66.44 |
9700 |
-0.006849 |
0.9945 |
|
| aquaculture |
11.44 |
9.114 |
1.255 |
0.2095 |
|
| city |
9.346 |
7.397 |
1.264 |
0.2064 |
|
| dredging_collect |
3.002 |
3.887 |
0.7722 |
0.44 |
|
| dredging_dump |
-6.606 |
4.142 |
-1.595 |
0.1107 |
|
| industry |
-1.924 |
1.846 |
-1.042 |
0.2973 |
|
| shipping_mooring |
8.09 |
6.575 |
1.23 |
0.2185 |
|
| shipping_traffic |
3.399 |
2.441 |
1.392 |
0.1639 |
|
| sewers_rain |
-5.321 |
4.111 |
-1.295 |
0.1955 |
|
| sewers_waste |
6.127 |
6.398 |
0.9577 |
0.3382 |
|
| wharves_city |
-10.15 |
8.193 |
-1.239 |
0.2155 |
|
| wharves_industry |
2.932 |
4.124 |
0.711 |
0.4771 |
|
| fisheries_trap |
0.577 |
0.3787 |
1.523 |
0.1277 |
|
| fisheries_trawl |
-7.864 |
11.24 |
-0.6995 |
0.4842 |
|
| fisheries_net |
-638.6 |
100581 |
-0.006349 |
0.9949 |
|
| fisheries_dredge |
1.405 |
1.029 |
1.366 |
0.1719 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
21.2 |
19.1 |
9.12 |
10.4 |
3.18 |
16.9 |
6.7 |
10.7 |
16.2 |
22.4 |
10.1 |
1.32 |
1.75 |
1 |
2.28 |

Lamprops quadriplicata
## SDM for: lamprops_quadriplicata
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-127.6 |
231428 |
-0.0005514 |
0.9996 |
|
| om |
-21.63 |
338176 |
-6.396e-05 |
0.9999 |
|
| gravel |
-1.938 |
252060 |
-7.69e-06 |
1 |
|
| silt |
-38.07 |
163499 |
-0.0002329 |
0.9998 |
|
| clay |
45.35 |
409897 |
0.0001106 |
0.9999 |
|
| arsenic |
59.89 |
181479 |
0.00033 |
0.9997 |
|
| cadmium |
1.642 |
115825 |
1.418e-05 |
1 |
|
| copper |
-33.93 |
242411 |
-0.00014 |
0.9999 |
|
| iron |
-136.1 |
451420 |
-0.0003014 |
0.9998 |
|
| manganese |
71.43 |
337247 |
0.0002118 |
0.9998 |
|
| mercury |
35.88 |
253134 |
0.0001417 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.05 |
2.18 |
3.68 |
1.11 |
2.02 |
1.87 |
3.79 |
4.07 |
3.98 |
2.65 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-223.8 |
399631 |
-0.0005599 |
0.9996 |
|
| aquaculture |
680.6 |
1783582 |
0.0003816 |
0.9997 |
|
| city |
-497.6 |
1488129 |
-0.0003344 |
0.9997 |
|
| dredging_collect |
405.2 |
2185261 |
0.0001854 |
0.9999 |
|
| dredging_dump |
244.8 |
717180 |
0.0003414 |
0.9997 |
|
| industry |
-125.7 |
915888 |
-0.0001373 |
0.9999 |
|
| shipping_mooring |
524.9 |
1853575 |
0.0002832 |
0.9998 |
|
| shipping_traffic |
-257.8 |
1768738 |
-0.0001457 |
0.9999 |
|
| sewers_rain |
99.46 |
3236096 |
3.073e-05 |
1 |
|
| sewers_waste |
419.6 |
4632917 |
9.057e-05 |
0.9999 |
|
| wharves_city |
195.7 |
1550384 |
0.0001262 |
0.9999 |
|
| wharves_industry |
-684.3 |
1965543 |
-0.0003481 |
0.9997 |
|
| fisheries_trap |
-38.66 |
988141 |
-3.912e-05 |
1 |
|
| fisheries_trawl |
31.7 |
193204 |
0.0001641 |
0.9999 |
|
| fisheries_net |
-2.812 |
262612 |
-1.071e-05 |
1 |
|
| fisheries_dredge |
10.72 |
355324 |
3.018e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
17.8 |
31.7 |
48.6 |
15.1 |
26.9 |
21 |
44.4 |
46 |
59.9 |
23.5 |
49.8 |
2.78 |
2.2 |
1.04 |
2.47 |

Lepeta caeca
## SDM for: lepeta_caeca
Abiotic parameters
## McFadden's pseudo-R2 is: 0.39
## Tjur's pseudo-R2 is: 0.25
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-410.2 |
85646 |
-0.004789 |
0.9962 |
|
| om |
-3.948 |
2.047 |
-1.929 |
0.05375 |
|
| gravel |
0.9993 |
0.5546 |
1.802 |
0.07155 |
|
| silt |
1.932 |
1.21 |
1.597 |
0.1102 |
|
| clay |
-2204 |
467358 |
-0.004715 |
0.9962 |
|
| arsenic |
-1.599 |
3.016 |
-0.5301 |
0.596 |
|
| cadmium |
0.9393 |
1.29 |
0.7282 |
0.4665 |
|
| copper |
2.132 |
1.63 |
1.307 |
0.1911 |
|
| iron |
-0.08409 |
2.052 |
-0.04097 |
0.9673 |
|
| manganese |
-2.695 |
2.961 |
-0.9101 |
0.3628 |
|
| mercury |
-2.088 |
1.783 |
-1.171 |
0.2416 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.73 |
1.61 |
2.17 |
1 |
1.41 |
1.82 |
2.17 |
2.2 |
2.19 |
1.66 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-887.9 |
1856751 |
-0.0004782 |
0.9996 |
|
| aquaculture |
-430 |
2642672 |
-0.0001627 |
0.9999 |
|
| city |
281.9 |
3659010 |
7.703e-05 |
0.9999 |
|
| dredging_collect |
176.4 |
1372489 |
0.0001285 |
0.9999 |
|
| dredging_dump |
-865.6 |
4984482 |
-0.0001736 |
0.9999 |
|
| industry |
61.96 |
601524 |
0.000103 |
0.9999 |
|
| shipping_mooring |
99.58 |
1389029 |
7.169e-05 |
0.9999 |
|
| shipping_traffic |
101.1 |
1117764 |
9.047e-05 |
0.9999 |
|
| sewers_rain |
1038 |
3732573 |
0.0002781 |
0.9998 |
|
| sewers_waste |
-2007 |
5354800 |
-0.0003749 |
0.9997 |
|
| wharves_city |
525.3 |
6434047 |
8.165e-05 |
0.9999 |
|
| wharves_industry |
-137.2 |
2431662 |
-5.643e-05 |
1 |
|
| fisheries_trap |
-46.48 |
307919 |
-0.0001509 |
0.9999 |
|
| fisheries_trawl |
-10.23 |
72445 |
-0.0001412 |
0.9999 |
|
| fisheries_net |
22.84 |
6459583 |
3.536e-06 |
1 |
|
| fisheries_dredge |
15.01 |
196423 |
7.642e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
50.7 |
83.2 |
36.1 |
117 |
11.7 |
29.1 |
26.9 |
79.7 |
93.1 |
147 |
60.9 |
3.68 |
3.23 |
1 |
3.87 |

Leucon leucon nasicoides
## SDM for: leucon_leucon_nasicoides
Abiotic parameters
## McFadden's pseudo-R2 is: 0.28
## Tjur's pseudo-R2 is: 0.32
## Pearson's pseudo-R2 is: 0.32
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-320.2 |
33855 |
-0.009459 |
0.9925 |
|
| om |
1.615 |
0.603 |
2.678 |
0.007413 |
* * |
| gravel |
-0.4022 |
0.3651 |
-1.102 |
0.2706 |
|
| silt |
-0.3783 |
0.5619 |
-0.6733 |
0.5007 |
|
| clay |
-1745 |
184739 |
-0.009444 |
0.9925 |
|
| arsenic |
0.3244 |
0.4388 |
0.7392 |
0.4598 |
|
| cadmium |
-0.5808 |
0.4745 |
-1.224 |
0.2209 |
|
| copper |
-0.6626 |
0.5433 |
-1.22 |
0.2226 |
|
| iron |
-0.3635 |
0.3658 |
-0.9938 |
0.3203 |
|
| manganese |
0.2702 |
0.608 |
0.4444 |
0.6568 |
|
| mercury |
0.6638 |
0.4925 |
1.348 |
0.1777 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.34 |
1.98 |
1 |
1.49 |
1.67 |
1.99 |
1.43 |
1.87 |
1.67 |

Influence indices
## McFadden's pseudo-R2 is: 0.4
## Tjur's pseudo-R2 is: 0.43
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-4.466 |
1.846 |
-2.419 |
0.01556 |
* |
| aquaculture |
6.603 |
3.41 |
1.936 |
0.05285 |
|
| city |
-22.52 |
12.87 |
-1.749 |
0.08027 |
|
| dredging_collect |
7.098 |
3.152 |
2.252 |
0.02434 |
* |
| dredging_dump |
-1.318 |
3.524 |
-0.3741 |
0.7083 |
|
| industry |
-2.258 |
1.541 |
-1.465 |
0.1429 |
|
| shipping_mooring |
18.82 |
6.696 |
2.811 |
0.004939 |
* * |
| shipping_traffic |
2.445 |
1.608 |
1.521 |
0.1283 |
|
| sewers_rain |
4.06 |
3.239 |
1.254 |
0.21 |
|
| sewers_waste |
-13.73 |
5.982 |
-2.294 |
0.02177 |
* |
| wharves_city |
12.93 |
10.36 |
1.248 |
0.2119 |
|
| wharves_industry |
-4.411 |
4.559 |
-0.9675 |
0.3333 |
|
| fisheries_trap |
-2.454 |
1.655 |
-1.483 |
0.138 |
|
| fisheries_trawl |
-0.05317 |
0.343 |
-0.155 |
0.8768 |
|
| fisheries_net |
0.487 |
2.51 |
0.194 |
0.8462 |
|
| fisheries_dredge |
0.6187 |
0.6818 |
0.9075 |
0.3642 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
13.1 |
33.9 |
9.28 |
10.7 |
3.3 |
24.9 |
5.06 |
11.3 |
22 |
29.3 |
13.6 |
1.19 |
1.46 |
1 |
1.67 |

Littorina littorea
## SDM for: littorina_littorea
Abiotic parameters
## McFadden's pseudo-R2 is: 0.52
## Tjur's pseudo-R2 is: 0.43
## Pearson's pseudo-R2 is: 0.47
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-382.7 |
89075 |
-0.004296 |
0.9966 |
|
| om |
-2.203 |
2.959 |
-0.7445 |
0.4566 |
|
| gravel |
1.003 |
1.336 |
0.7509 |
0.4527 |
|
| silt |
1.443 |
1.581 |
0.9125 |
0.3615 |
|
| clay |
-2034 |
486068 |
-0.004185 |
0.9967 |
|
| arsenic |
-1.353 |
3.2 |
-0.4227 |
0.6725 |
|
| cadmium |
4.213 |
3.224 |
1.307 |
0.1913 |
|
| copper |
5.345 |
4.058 |
1.317 |
0.1878 |
|
| iron |
-5.683 |
5.524 |
-1.029 |
0.3036 |
|
| manganese |
-5.868 |
5.174 |
-1.134 |
0.2567 |
|
| mercury |
-2.599 |
3.533 |
-0.7357 |
0.4619 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.62 |
1.6 |
1.69 |
1 |
2.64 |
4.63 |
5.49 |
5.65 |
2.98 |
1.75 |

Influence indices
## McFadden's pseudo-R2 is: -7.31
## Tjur's pseudo-R2 is: 0.64
## Pearson's pseudo-R2 is: 0.31
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.241e+15 |
7496963 |
-298928083 |
0 |
* * * |
| aquaculture |
6.476e+14 |
66771996 |
9699157 |
0 |
* * * |
| city |
4.011e+15 |
60515998 |
66271739 |
0 |
* * * |
| dredging_collect |
1.567e+15 |
47886179 |
32718840 |
0 |
* * * |
| dredging_dump |
3.17e+14 |
55589796 |
5702620 |
0 |
* * * |
| industry |
-9.946e+14 |
30193191 |
-32940931 |
0 |
* * * |
| shipping_mooring |
1.77e+14 |
51224704 |
3455393 |
0 |
* * * |
| shipping_traffic |
1.316e+15 |
22815885 |
57668144 |
0 |
* * * |
| sewers_rain |
-6.65e+14 |
67164046 |
-9900944 |
0 |
* * * |
| sewers_waste |
-4.773e+14 |
90359677 |
-5281913 |
0 |
* * * |
| wharves_city |
-4.452e+15 |
72465526 |
-61433156 |
0 |
* * * |
| wharves_industry |
-1.523e+15 |
79067645 |
-19261641 |
0 |
* * * |
| fisheries_trap |
-3.431e+14 |
7277539 |
-47139753 |
0 |
* * * |
| fisheries_trawl |
-6.619e+14 |
8821984 |
-75027511 |
0 |
* * * |
| fisheries_net |
-2.349e+14 |
7219163 |
-32537781 |
0 |
* * * |
| fisheries_dredge |
-1.69e+15 |
19454028 |
-86868875 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Lumbrineridae spp
## SDM for: lumbrineridae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.73
## Tjur's pseudo-R2 is: 0.58
## Pearson's pseudo-R2 is: 0.55
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-35.04 |
3717 |
-0.009427 |
0.9925 |
|
| om |
5.15 |
7.01 |
0.7347 |
0.4625 |
|
| gravel |
-1.202 |
3486 |
-0.0003448 |
0.9997 |
|
| silt |
10.18 |
14.35 |
0.7093 |
0.4781 |
|
| clay |
-39.14 |
20011 |
-0.001956 |
0.9984 |
|
| arsenic |
-28.34 |
41.06 |
-0.6901 |
0.4902 |
|
| cadmium |
4.427 |
4.451 |
0.9944 |
0.32 |
|
| copper |
-1.98 |
3.299 |
-0.6004 |
0.5483 |
|
| iron |
2.055 |
3.913 |
0.5253 |
0.5994 |
|
| manganese |
-1.216 |
6.657 |
-0.1827 |
0.855 |
|
| mercury |
-6.542 |
8.093 |
-0.8083 |
0.4189 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.65 |
1 |
5.12 |
1 |
6.19 |
3.84 |
1.41 |
2.35 |
2.39 |
2.49 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-147.9 |
370319 |
-0.0003995 |
0.9997 |
|
| aquaculture |
-193.7 |
2871761 |
-6.746e-05 |
0.9999 |
|
| city |
16.17 |
2532646 |
6.384e-06 |
1 |
|
| dredging_collect |
-11.86 |
3264151 |
-3.633e-06 |
1 |
|
| dredging_dump |
417.1 |
2883451 |
0.0001447 |
0.9999 |
|
| industry |
98.7 |
740348 |
0.0001333 |
0.9999 |
|
| shipping_mooring |
-52.27 |
2146387 |
-2.435e-05 |
1 |
|
| shipping_traffic |
-216.2 |
554826 |
-0.0003896 |
0.9997 |
|
| sewers_rain |
528.7 |
2288835 |
0.000231 |
0.9998 |
|
| sewers_waste |
-618.8 |
3338517 |
-0.0001853 |
0.9999 |
|
| wharves_city |
-170.9 |
2584897 |
-6.611e-05 |
0.9999 |
|
| wharves_industry |
-230.1 |
5547876 |
-4.148e-05 |
1 |
|
| fisheries_trap |
30.32 |
293547 |
0.0001033 |
0.9999 |
|
| fisheries_trawl |
13.37 |
116131 |
0.0001151 |
0.9999 |
|
| fisheries_net |
-0.12 |
160878 |
-7.462e-07 |
1 |
|
| fisheries_dredge |
-98.36 |
644362 |
-0.0001527 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
83 |
41.9 |
66.4 |
45.9 |
8.03 |
47.8 |
11 |
53.1 |
92 |
47.7 |
104 |
10.3 |
3.26 |
1.05 |
9.06 |

Lysianassidae spp
## SDM for: lysianassidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-349.9 |
719518 |
-0.0004862 |
0.9996 |
|
| om |
-96.82 |
242602 |
-0.0003991 |
0.9997 |
|
| gravel |
-249.9 |
767392 |
-0.0003256 |
0.9997 |
|
| silt |
101 |
261474 |
0.0003864 |
0.9997 |
|
| clay |
27.24 |
133190 |
0.0002045 |
0.9998 |
|
| arsenic |
-249.2 |
932312 |
-0.0002673 |
0.9998 |
|
| cadmium |
-6.558 |
187757 |
-3.493e-05 |
1 |
|
| copper |
52.95 |
379490 |
0.0001395 |
0.9999 |
|
| iron |
-17.1 |
597125 |
-2.864e-05 |
1 |
|
| manganese |
12.54 |
687137 |
1.825e-05 |
1 |
|
| mercury |
-129.1 |
278648 |
-0.0004635 |
0.9996 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.57 |
1.71 |
3.13 |
1.49 |
2.7 |
2.7 |
4.5 |
6 |
4.35 |
2.82 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-115.8 |
463451 |
-0.0002498 |
0.9998 |
|
| aquaculture |
233.6 |
8135597 |
2.871e-05 |
1 |
|
| city |
349 |
6258054 |
5.576e-05 |
1 |
|
| dredging_collect |
48.91 |
2745551 |
1.781e-05 |
1 |
|
| dredging_dump |
-280.4 |
5399354 |
-5.194e-05 |
1 |
|
| industry |
-53.64 |
1318456 |
-4.069e-05 |
1 |
|
| shipping_mooring |
127.2 |
6420447 |
1.981e-05 |
1 |
|
| shipping_traffic |
81.35 |
3391907 |
2.398e-05 |
1 |
|
| sewers_rain |
-163.6 |
3652552 |
-4.478e-05 |
1 |
|
| sewers_waste |
145.7 |
7481054 |
1.948e-05 |
1 |
|
| wharves_city |
-359 |
6124051 |
-5.863e-05 |
1 |
|
| wharves_industry |
251.4 |
2131870 |
0.0001179 |
0.9999 |
|
| fisheries_trap |
14.15 |
185408 |
7.631e-05 |
0.9999 |
|
| fisheries_trawl |
2.76 |
112433 |
2.454e-05 |
1 |
|
| fisheries_net |
10.57 |
186491 |
5.668e-05 |
1 |
|
| fisheries_dredge |
20.01 |
3021091 |
6.625e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
93.6 |
111 |
52.4 |
106 |
20 |
76.6 |
66.4 |
48.5 |
84.9 |
118 |
41.6 |
3.03 |
1.49 |
1.22 |
30.8 |

Macoma calcarea
## SDM for: macoma_calcarea
Abiotic parameters
## McFadden's pseudo-R2 is: 0.16
## Tjur's pseudo-R2 is: 0.17
## Pearson's pseudo-R2 is: 0.17
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
1.643 |
0.3659 |
4.489 |
7.142e-06 |
* * * |
| om |
1.271 |
0.6414 |
1.982 |
0.04749 |
* |
| gravel |
-0.4591 |
0.3209 |
-1.431 |
0.1525 |
|
| silt |
-0.187 |
0.6078 |
-0.3077 |
0.7583 |
|
| clay |
-0.3593 |
0.708 |
-0.5075 |
0.6118 |
|
| arsenic |
-0.06127 |
0.4142 |
-0.1479 |
0.8824 |
|
| cadmium |
-0.4431 |
0.4017 |
-1.103 |
0.2701 |
|
| copper |
-0.7986 |
0.5216 |
-1.531 |
0.1258 |
|
| iron |
-0.3209 |
0.4634 |
-0.6923 |
0.4887 |
|
| manganese |
0.4665 |
0.5369 |
0.8688 |
0.3849 |
|
| mercury |
-0.2265 |
0.4687 |
-0.4833 |
0.6289 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.03 |
1.52 |
2.08 |
1.11 |
1.48 |
1.37 |
1.89 |
1.49 |
1.74 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0.21
## Tjur's pseudo-R2 is: 0.24
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
2.243 |
0.6817 |
3.29 |
0.001002 |
* * |
| aquaculture |
-3.01 |
3.444 |
-0.8739 |
0.3822 |
|
| city |
1.137 |
2.565 |
0.4432 |
0.6576 |
|
| dredging_collect |
7.379 |
4.569 |
1.615 |
0.1063 |
|
| dredging_dump |
3.446 |
2.272 |
1.517 |
0.1293 |
|
| industry |
1.773 |
1.701 |
1.042 |
0.2973 |
|
| shipping_mooring |
0.2284 |
2.123 |
0.1076 |
0.9143 |
|
| shipping_traffic |
0.5625 |
1.009 |
0.5574 |
0.5772 |
|
| sewers_rain |
2.783 |
2.692 |
1.034 |
0.3012 |
|
| sewers_waste |
-5.062 |
3.774 |
-1.341 |
0.1798 |
|
| wharves_city |
-1.454 |
3.087 |
-0.4711 |
0.6376 |
|
| wharves_industry |
-11.4 |
5.684 |
-2.005 |
0.04493 |
* |
| fisheries_trap |
-0.2775 |
0.2978 |
-0.9318 |
0.3515 |
|
| fisheries_trawl |
0.2448 |
0.4544 |
0.5388 |
0.59 |
|
| fisheries_net |
0.5933 |
2.91 |
0.2039 |
0.8384 |
|
| fisheries_dredge |
1.712 |
1.865 |
0.918 |
0.3586 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.6 |
8.79 |
15.6 |
7.88 |
6.37 |
6.7 |
3.61 |
8.37 |
11.8 |
10 |
19.8 |
1.3 |
1.57 |
1 |
2.79 |

Maera danae
## SDM for: maera_danae
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-645.8 |
3768764 |
-0.0001714 |
0.9999 |
|
| om |
-266.9 |
611132 |
-0.0004367 |
0.9997 |
|
| gravel |
-134.3 |
743421 |
-0.0001806 |
0.9999 |
|
| silt |
19.72 |
357831 |
5.512e-05 |
1 |
|
| clay |
1.933 |
19767865 |
9.78e-08 |
1 |
|
| arsenic |
-116.4 |
470332 |
-0.0002475 |
0.9998 |
|
| cadmium |
302.8 |
679239 |
0.0004459 |
0.9996 |
|
| copper |
422 |
875479 |
0.000482 |
0.9996 |
|
| iron |
-592.8 |
1397448 |
-0.0004242 |
0.9997 |
|
| manganese |
-137.3 |
757012 |
-0.0001814 |
0.9999 |
|
| mercury |
60.15 |
198975 |
0.0003023 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.7 |
1.36 |
5.04 |
1.12 |
8.45 |
18.9 |
29 |
29.7 |
2.96 |
2.61 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-71.01 |
140791 |
-0.0005043 |
0.9996 |
|
| aquaculture |
1.465 |
1423813 |
1.029e-06 |
1 |
|
| city |
-161.8 |
1043763 |
-0.000155 |
0.9999 |
|
| dredging_collect |
3.309 |
1272201 |
2.601e-06 |
1 |
|
| dredging_dump |
-26.73 |
1302138 |
-2.053e-05 |
1 |
|
| industry |
74.63 |
725722 |
0.0001028 |
0.9999 |
|
| shipping_mooring |
-18.84 |
2029726 |
-9.282e-06 |
1 |
|
| shipping_traffic |
-73.09 |
1305542 |
-5.599e-05 |
1 |
|
| sewers_rain |
30 |
1337511 |
2.243e-05 |
1 |
|
| sewers_waste |
84.55 |
1870889 |
4.519e-05 |
1 |
|
| wharves_city |
187 |
1966847 |
9.506e-05 |
0.9999 |
|
| wharves_industry |
-75.07 |
1924373 |
-3.901e-05 |
1 |
|
| fisheries_trap |
7.911 |
79350 |
9.97e-05 |
0.9999 |
|
| fisheries_trawl |
4.787 |
214578 |
2.231e-05 |
1 |
|
| fisheries_net |
1.25 |
121769 |
1.026e-05 |
1 |
|
| fisheries_dredge |
0.9453 |
383211 |
2.467e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.6 |
22.8 |
26.7 |
27.9 |
15 |
24.3 |
26.1 |
16 |
21.8 |
48.2 |
38.9 |
5.76 |
3.25 |
1.3 |
2.72 |

Maldane sarsi
## SDM for: maldane_sarsi
Abiotic parameters
## McFadden's pseudo-R2 is: -6.59
## Tjur's pseudo-R2 is: 0
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.92e+15 |
7978909 |
-240653949 |
0 |
* * * |
| om |
-2.389e+14 |
14257309 |
-16759234 |
0 |
* * * |
| gravel |
-2.197e+13 |
8714549 |
-2521177 |
0 |
* * * |
| silt |
9.546e+14 |
15274819 |
62495942 |
0 |
* * * |
| clay |
-1.683e+15 |
21858102 |
-76993339 |
0 |
* * * |
| arsenic |
-2.395e+14 |
11951982 |
-20042071 |
0 |
* * * |
| cadmium |
-7.473e+14 |
10400421 |
-71850925 |
0 |
* * * |
| copper |
1.349e+15 |
13687347 |
98584389 |
0 |
* * * |
| iron |
-5.594e+14 |
9620115 |
-58152424 |
0 |
* * * |
| manganese |
3.474e+12 |
14288707 |
243142 |
0 |
* * * |
| mercury |
-4.07e+14 |
12068606 |
-33720427 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-81.54 |
137742 |
-0.000592 |
0.9995 |
|
| aquaculture |
-54.11 |
2385465 |
-2.269e-05 |
1 |
|
| city |
-105.3 |
677852 |
-0.0001554 |
0.9999 |
|
| dredging_collect |
-329.6 |
844043 |
-0.0003905 |
0.9997 |
|
| dredging_dump |
-3.8 |
1380487 |
-2.752e-06 |
1 |
|
| industry |
25.97 |
534623 |
4.858e-05 |
1 |
|
| shipping_mooring |
-122.6 |
1270886 |
-9.646e-05 |
0.9999 |
|
| shipping_traffic |
-35.53 |
297053 |
-0.0001196 |
0.9999 |
|
| sewers_rain |
0.266 |
1531880 |
1.736e-07 |
1 |
|
| sewers_waste |
85.02 |
2227575 |
3.817e-05 |
1 |
|
| wharves_city |
177.3 |
996571 |
0.0001779 |
0.9999 |
|
| wharves_industry |
299.4 |
1342407 |
0.000223 |
0.9998 |
|
| fisheries_trap |
-3.824 |
167868 |
-2.278e-05 |
1 |
|
| fisheries_trawl |
-11.76 |
619325 |
-1.898e-05 |
1 |
|
| fisheries_net |
7.32 |
101990 |
7.178e-05 |
0.9999 |
|
| fisheries_dredge |
1.246 |
1136493 |
1.096e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
40.4 |
10.7 |
17.3 |
25.5 |
11.4 |
20.8 |
6.33 |
34.1 |
49.5 |
15.7 |
28.5 |
1.4 |
5.82 |
1.09 |
8.91 |

Maldanidae spp
## SDM for: maldanidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.18
## Tjur's pseudo-R2 is: 0.16
## Pearson's pseudo-R2 is: 0.15
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-9.236 |
736.6 |
-0.01254 |
0.99 |
|
| om |
0.2894 |
0.5228 |
0.5536 |
0.5799 |
|
| gravel |
-28.91 |
2630 |
-0.01099 |
0.9912 |
|
| silt |
0.3825 |
0.6283 |
0.6088 |
0.5427 |
|
| clay |
-0.8682 |
1.489 |
-0.5831 |
0.5598 |
|
| arsenic |
-0.2267 |
0.4389 |
-0.5166 |
0.6055 |
|
| cadmium |
-0.5311 |
0.5208 |
-1.02 |
0.3078 |
|
| copper |
0.4499 |
0.5702 |
0.7891 |
0.4301 |
|
| iron |
-0.2214 |
0.4848 |
-0.4566 |
0.6479 |
|
| manganese |
-0.2024 |
0.5672 |
-0.3568 |
0.7212 |
|
| mercury |
0.4984 |
0.4506 |
1.106 |
0.2687 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.88 |
1 |
1.97 |
1.03 |
1.57 |
1.74 |
2.07 |
1.58 |
1.9 |
1.55 |

Influence indices
## McFadden's pseudo-R2 is: 0.32
## Tjur's pseudo-R2 is: 0.34
## Pearson's pseudo-R2 is: 0.33
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-77.43 |
8287 |
-0.009343 |
0.9925 |
|
| aquaculture |
4.546 |
2.928 |
1.553 |
0.1205 |
|
| city |
-19.81 |
10.29 |
-1.925 |
0.05422 |
|
| dredging_collect |
4.522 |
2.524 |
1.791 |
0.07323 |
|
| dredging_dump |
-2.662 |
2.844 |
-0.936 |
0.3493 |
|
| industry |
-0.7146 |
1.253 |
-0.5701 |
0.5686 |
|
| shipping_mooring |
11.43 |
4.166 |
2.744 |
0.006075 |
* * |
| shipping_traffic |
0.6896 |
1.244 |
0.5541 |
0.5795 |
|
| sewers_rain |
1.686 |
3.786 |
0.4452 |
0.6562 |
|
| sewers_waste |
-4.832 |
5.353 |
-0.9027 |
0.3667 |
|
| wharves_city |
15.34 |
8.609 |
1.782 |
0.07482 |
|
| wharves_industry |
-3.486 |
4.252 |
-0.82 |
0.4122 |
|
| fisheries_trap |
0.2032 |
0.3795 |
0.5354 |
0.5924 |
|
| fisheries_trawl |
-0.06257 |
0.3559 |
-0.1758 |
0.8604 |
|
| fisheries_net |
-774.2 |
85927 |
-0.00901 |
0.9928 |
|
| fisheries_dredge |
0.06996 |
0.7317 |
0.09561 |
0.9238 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.2 |
29.6 |
7.65 |
8.81 |
2.77 |
14.6 |
3.86 |
13.2 |
19.3 |
26.5 |
13 |
1.13 |
1.38 |
1 |
1.68 |

Monoculopsis longicornis
## SDM for: monoculopsis_longicornis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.63
## Tjur's pseudo-R2 is: 0.48
## Pearson's pseudo-R2 is: 0.43
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-86.66 |
38773 |
-0.002235 |
0.9982 |
|
| om |
-1.559 |
3.285 |
-0.4747 |
0.635 |
|
| gravel |
-28.24 |
26931 |
-0.001049 |
0.9992 |
|
| silt |
0.01079 |
2.344 |
0.004602 |
0.9963 |
|
| clay |
-387.6 |
208414 |
-0.00186 |
0.9985 |
|
| arsenic |
1.75 |
2.683 |
0.6522 |
0.5143 |
|
| cadmium |
-3.345 |
3.536 |
-0.9461 |
0.3441 |
|
| copper |
4.331 |
3.089 |
1.402 |
0.1609 |
|
| iron |
-3.728 |
5.918 |
-0.63 |
0.5287 |
|
| manganese |
-3.273 |
11.72 |
-0.2792 |
0.7801 |
|
| mercury |
-1.005 |
3.995 |
-0.2515 |
0.8014 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.57 |
1 |
2.08 |
1 |
1.78 |
2.02 |
2.21 |
3.62 |
3.96 |
2.49 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-172.4 |
967725 |
-0.0001782 |
0.9999 |
|
| aquaculture |
-390 |
12814618 |
-3.043e-05 |
1 |
|
| city |
74.86 |
6308431 |
1.187e-05 |
1 |
|
| dredging_collect |
552.1 |
3103581 |
0.0001779 |
0.9999 |
|
| dredging_dump |
659 |
6308807 |
0.0001045 |
0.9999 |
|
| industry |
128.5 |
4011008 |
3.203e-05 |
1 |
|
| shipping_mooring |
-117.2 |
6354516 |
-1.844e-05 |
1 |
|
| shipping_traffic |
45.96 |
6798871 |
6.759e-06 |
1 |
|
| sewers_rain |
499.8 |
7902243 |
6.325e-05 |
0.9999 |
|
| sewers_waste |
-623.7 |
15087169 |
-4.134e-05 |
1 |
|
| wharves_city |
-162 |
6443652 |
-2.514e-05 |
1 |
|
| wharves_industry |
-1230 |
11358651 |
-0.0001083 |
0.9999 |
|
| fisheries_trap |
-2.09 |
435485 |
-4.799e-06 |
1 |
|
| fisheries_trawl |
29.63 |
8018744 |
3.695e-06 |
1 |
|
| fisheries_net |
7.453 |
401172 |
1.858e-05 |
1 |
|
| fisheries_dredge |
-23.63 |
1800714 |
-1.312e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
120 |
64.4 |
33.7 |
67.1 |
38.1 |
72.6 |
79.9 |
101 |
180 |
68.3 |
124 |
2.19 |
66.5 |
1.6 |
37.9 |

Muculus musculus discors
## SDM for: muculus_musculus_discors
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-182.6 |
473133 |
-0.0003859 |
0.9997 |
|
| om |
-134.2 |
358347 |
-0.0003744 |
0.9997 |
|
| gravel |
-4.482 |
286106 |
-1.566e-05 |
1 |
|
| silt |
64.12 |
285563 |
0.0002245 |
0.9998 |
|
| clay |
-105.7 |
1398946 |
-7.555e-05 |
0.9999 |
|
| arsenic |
5.142 |
431968 |
1.19e-05 |
1 |
|
| cadmium |
-40.83 |
219759 |
-0.0001858 |
0.9999 |
|
| copper |
62.63 |
258197 |
0.0002426 |
0.9998 |
|
| iron |
9.686 |
117058 |
8.274e-05 |
0.9999 |
|
| manganese |
-19.25 |
328394 |
-5.861e-05 |
1 |
|
| mercury |
-69.44 |
301137 |
-0.0002306 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.88 |
1.28 |
4.22 |
1.51 |
3.79 |
3.13 |
4.32 |
1.92 |
4.01 |
3.3 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-35.16 |
72323 |
-0.0004862 |
0.9996 |
|
| aquaculture |
-24 |
1358385 |
-1.767e-05 |
1 |
|
| city |
-12.59 |
1297078 |
-9.708e-06 |
1 |
|
| dredging_collect |
-14.92 |
851895 |
-1.751e-05 |
1 |
|
| dredging_dump |
-6.445 |
662645 |
-9.726e-06 |
1 |
|
| industry |
11.9 |
525131 |
2.266e-05 |
1 |
|
| shipping_mooring |
-27.69 |
1438980 |
-1.924e-05 |
1 |
|
| shipping_traffic |
-18.11 |
338086 |
-5.356e-05 |
1 |
|
| sewers_rain |
-19.75 |
1022136 |
-1.932e-05 |
1 |
|
| sewers_waste |
24.54 |
1349796 |
1.818e-05 |
1 |
|
| wharves_city |
28.32 |
1807498 |
1.567e-05 |
1 |
|
| wharves_industry |
31.49 |
777801 |
4.048e-05 |
1 |
|
| fisheries_trap |
-0.6731 |
83030 |
-8.106e-06 |
1 |
|
| fisheries_trawl |
10.88 |
67868 |
0.0001604 |
0.9999 |
|
| fisheries_net |
3.765 |
70648 |
5.329e-05 |
1 |
|
| fisheries_dredge |
0.6407 |
190947 |
3.355e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
27.5 |
18.2 |
14.2 |
11 |
9.45 |
23.7 |
5.79 |
15.1 |
21.9 |
25.4 |
12.8 |
1.63 |
4.13 |
1.25 |
2.35 |

Mytilus sp
## SDM for: mytilus_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.2
## Tjur's pseudo-R2 is: 0.17
## Pearson's pseudo-R2 is: 0.16
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.88 |
0.688 |
-4.186 |
2.841e-05 |
* * * |
| om |
-0.1948 |
0.7785 |
-0.2502 |
0.8024 |
|
| gravel |
0.1476 |
0.4662 |
0.3167 |
0.7515 |
|
| silt |
0.6068 |
0.787 |
0.771 |
0.4407 |
|
| clay |
-0.9339 |
1.514 |
-0.6167 |
0.5374 |
|
| arsenic |
-1.527 |
1.144 |
-1.335 |
0.182 |
|
| cadmium |
0.5376 |
0.5464 |
0.9839 |
0.3251 |
|
| copper |
2.405 |
0.898 |
2.678 |
0.007406 |
* * |
| iron |
-2.358 |
1.374 |
-1.716 |
0.08613 |
|
| manganese |
0.2591 |
1.014 |
0.2556 |
0.7983 |
|
| mercury |
-2.226 |
1.131 |
-1.968 |
0.0491 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.97 |
1.21 |
1.94 |
1.07 |
1.75 |
1.66 |
2.77 |
2.95 |
1.95 |
1.83 |

Influence indices
## McFadden's pseudo-R2 is: 0.35
## Tjur's pseudo-R2 is: 0.3
## Pearson's pseudo-R2 is: 0.29
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-64.65 |
7059 |
-0.009159 |
0.9927 |
|
| aquaculture |
6.408 |
6.141 |
1.044 |
0.2967 |
|
| city |
1.343 |
3.707 |
0.3622 |
0.7172 |
|
| dredging_collect |
-0.17 |
3.938 |
-0.04317 |
0.9656 |
|
| dredging_dump |
3.818 |
6.346 |
0.6016 |
0.5475 |
|
| industry |
-3.551 |
2.632 |
-1.349 |
0.1773 |
|
| shipping_mooring |
1.824 |
4.812 |
0.379 |
0.7047 |
|
| shipping_traffic |
-2.772 |
1.842 |
-1.504 |
0.1325 |
|
| sewers_rain |
3.33 |
5.371 |
0.62 |
0.5352 |
|
| sewers_waste |
-0.7764 |
8.056 |
-0.09638 |
0.9232 |
|
| wharves_city |
-2.294 |
5.588 |
-0.4105 |
0.6815 |
|
| wharves_industry |
1.977 |
5.775 |
0.3424 |
0.7321 |
|
| fisheries_trap |
-0.4839 |
0.4738 |
-1.021 |
0.3071 |
|
| fisheries_trawl |
0.2315 |
0.5303 |
0.4366 |
0.6624 |
|
| fisheries_net |
-631 |
73197 |
-0.008621 |
0.9931 |
|
| fisheries_dredge |
0.5779 |
1.306 |
0.4426 |
0.658 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
15.6 |
11.3 |
10.5 |
17.6 |
7.11 |
11.6 |
5.54 |
16 |
22.5 |
16.2 |
15.9 |
1.08 |
1.33 |
1 |
2.22 |

Nematoda
## SDM for: nematoda
Abiotic parameters
## McFadden's pseudo-R2 is: 0.47
## Tjur's pseudo-R2 is: 0.54
## Pearson's pseudo-R2 is: 0.55
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.457 |
1.067 |
-2.302 |
0.02132 |
* |
| om |
-0.8545 |
0.7232 |
-1.182 |
0.2374 |
|
| gravel |
-0.2797 |
0.3777 |
-0.7405 |
0.459 |
|
| silt |
-0.6092 |
0.7289 |
-0.8358 |
0.4033 |
|
| clay |
-9.586 |
5.489 |
-1.746 |
0.08074 |
|
| arsenic |
1.045 |
0.536 |
1.95 |
0.05112 |
|
| cadmium |
-1.131 |
0.5194 |
-2.177 |
0.02946 |
* |
| copper |
-1.242 |
0.8724 |
-1.424 |
0.1546 |
|
| iron |
-2.346 |
1.254 |
-1.871 |
0.06139 |
|
| manganese |
1.621 |
0.8759 |
1.851 |
0.06417 |
|
| mercury |
0.3263 |
0.5635 |
0.5791 |
0.5625 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.78 |
1.34 |
2.09 |
1.28 |
1.61 |
1.44 |
2.37 |
2.51 |
2.55 |
1.67 |

Influence indices
## McFadden's pseudo-R2 is: 0.33
## Tjur's pseudo-R2 is: 0.39
## Pearson's pseudo-R2 is: 0.38
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-0.2484 |
0.4321 |
-0.5749 |
0.5654 |
|
| aquaculture |
-1.278 |
2.837 |
-0.4504 |
0.6525 |
|
| city |
-1.194 |
2.648 |
-0.4508 |
0.6522 |
|
| dredging_collect |
1.334 |
2.115 |
0.6306 |
0.5283 |
|
| dredging_dump |
-1.361 |
2.363 |
-0.5761 |
0.5646 |
|
| industry |
0.4921 |
1.263 |
0.3896 |
0.6969 |
|
| shipping_mooring |
-3.203 |
2.485 |
-1.289 |
0.1974 |
|
| shipping_traffic |
2.436 |
1.385 |
1.758 |
0.07873 |
|
| sewers_rain |
-1.229 |
2.759 |
-0.4454 |
0.656 |
|
| sewers_waste |
2.189 |
3.69 |
0.5934 |
0.5529 |
|
| wharves_city |
1.855 |
3.385 |
0.548 |
0.5837 |
|
| wharves_industry |
-3.37 |
3.321 |
-1.015 |
0.3102 |
|
| fisheries_trap |
-0.112 |
0.3071 |
-0.3647 |
0.7153 |
|
| fisheries_trawl |
-0.6734 |
0.4 |
-1.684 |
0.09226 |
|
| fisheries_net |
0.5368 |
2.741 |
0.1958 |
0.8448 |
|
| fisheries_dredge |
0.1026 |
1.081 |
0.09483 |
0.9244 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.7 |
9 |
6.77 |
7.41 |
4.82 |
8.07 |
5.09 |
10 |
13.4 |
10.6 |
10.9 |
1.09 |
1.41 |
1 |
2.02 |

Nemertea
## SDM for: nemertea
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-92.72 |
150483 |
-0.0006162 |
0.9995 |
|
| om |
-51.01 |
168846 |
-0.0003021 |
0.9998 |
|
| gravel |
26.9 |
70521 |
0.0003815 |
0.9997 |
|
| silt |
22.7 |
175221 |
0.0001296 |
0.9999 |
|
| clay |
2.983 |
256019 |
1.165e-05 |
1 |
|
| arsenic |
-38.2 |
199649 |
-0.0001913 |
0.9998 |
|
| cadmium |
29.85 |
97550 |
0.000306 |
0.9998 |
|
| copper |
30.54 |
185178 |
0.0001649 |
0.9999 |
|
| iron |
7.023 |
449909 |
1.561e-05 |
1 |
|
| manganese |
-28.08 |
250267 |
-0.0001122 |
0.9999 |
|
| mercury |
17.88 |
178947 |
9.994e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.01 |
3.95 |
3.8 |
1.1 |
2.74 |
2.8 |
4.52 |
5.78 |
4.55 |
3.87 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-67.54 |
140643 |
-0.0004802 |
0.9996 |
|
| aquaculture |
71.34 |
4842619 |
1.473e-05 |
1 |
|
| city |
145.3 |
10402474 |
1.397e-05 |
1 |
|
| dredging_collect |
77.29 |
774144 |
9.983e-05 |
0.9999 |
|
| dredging_dump |
-69.13 |
2155231 |
-3.207e-05 |
1 |
|
| industry |
46.71 |
2178614 |
2.144e-05 |
1 |
|
| shipping_mooring |
71.66 |
2724611 |
2.63e-05 |
1 |
|
| shipping_traffic |
44.96 |
2905192 |
1.548e-05 |
1 |
|
| sewers_rain |
-7.165 |
4554958 |
-1.573e-06 |
1 |
|
| sewers_waste |
13.34 |
2622642 |
5.087e-06 |
1 |
|
| wharves_city |
-175.1 |
10700829 |
-1.637e-05 |
1 |
|
| wharves_industry |
-93.09 |
1857286 |
-5.012e-05 |
1 |
|
| fisheries_trap |
6.767 |
578628 |
1.17e-05 |
1 |
|
| fisheries_trawl |
-4.491 |
150280 |
-2.989e-05 |
1 |
|
| fisheries_net |
2.735 |
404568 |
6.76e-06 |
1 |
|
| fisheries_dredge |
-8.065 |
912207 |
-8.842e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
78.2 |
177 |
16.8 |
40.9 |
43.9 |
52.4 |
45.4 |
67.8 |
41.7 |
170 |
36.4 |
8.18 |
4.68 |
4.35 |
5.38 |

Neoleanira tetragona
## SDM for: neoleanira_tetragona
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-114.9 |
238426 |
-0.0004818 |
0.9996 |
|
| om |
4.49 |
777544 |
5.774e-06 |
1 |
|
| gravel |
-11.67 |
341497 |
-3.418e-05 |
1 |
|
| silt |
-23.42 |
276639 |
-8.466e-05 |
0.9999 |
|
| clay |
24.81 |
448080 |
5.537e-05 |
1 |
|
| arsenic |
41.32 |
263325 |
0.0001569 |
0.9999 |
|
| cadmium |
32.88 |
309826 |
0.0001061 |
0.9999 |
|
| copper |
4.492 |
353370 |
1.271e-05 |
1 |
|
| iron |
-31.99 |
566876 |
-5.643e-05 |
1 |
|
| manganese |
-90 |
805879 |
-0.0001117 |
0.9999 |
|
| mercury |
-24.23 |
533750 |
-4.539e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.29 |
1.3 |
3.19 |
1.12 |
1.95 |
3.04 |
3.38 |
3.58 |
5.21 |
2.24 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-112.7 |
232083 |
-0.0004858 |
0.9996 |
|
| aquaculture |
166.6 |
1386722 |
0.0001201 |
0.9999 |
|
| city |
-26.46 |
3025269 |
-8.746e-06 |
1 |
|
| dredging_collect |
270.6 |
1573361 |
0.000172 |
0.9999 |
|
| dredging_dump |
374.1 |
1185175 |
0.0003157 |
0.9997 |
|
| industry |
-22.15 |
1650474 |
-1.342e-05 |
1 |
|
| shipping_mooring |
253.7 |
943754 |
0.0002688 |
0.9998 |
|
| shipping_traffic |
-53.53 |
1143947 |
-4.68e-05 |
1 |
|
| sewers_rain |
247.1 |
1710636 |
0.0001445 |
0.9999 |
|
| sewers_waste |
-220.4 |
2920215 |
-7.549e-05 |
0.9999 |
|
| wharves_city |
-140.7 |
2837107 |
-4.96e-05 |
1 |
|
| wharves_industry |
-654.1 |
2237862 |
-0.0002923 |
0.9998 |
|
| fisheries_trap |
-27.78 |
264305 |
-0.0001051 |
0.9999 |
|
| fisheries_trawl |
5.439 |
410747 |
1.324e-05 |
1 |
|
| fisheries_net |
0.5491 |
174955 |
3.139e-06 |
1 |
|
| fisheries_dredge |
-10.36 |
321942 |
-3.218e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
14.8 |
39.7 |
22.8 |
17.1 |
30.9 |
9.77 |
19.6 |
19.1 |
28.9 |
32.9 |
33.8 |
1.26 |
3.14 |
1.14 |
2.07 |

Nephtyidae spp
## SDM for: nephtyidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.33
## Tjur's pseudo-R2 is: 0.24
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-129.7 |
22067 |
-0.005877 |
0.9953 |
|
| om |
-1.639 |
1.008 |
-1.627 |
0.1038 |
|
| gravel |
-32.36 |
16686 |
-0.001939 |
0.9985 |
|
| silt |
0.8808 |
0.8661 |
1.017 |
0.3092 |
|
| clay |
-638.5 |
117715 |
-0.005424 |
0.9957 |
|
| arsenic |
1.07 |
0.7221 |
1.482 |
0.1383 |
|
| cadmium |
-0.1174 |
0.6415 |
-0.183 |
0.8548 |
|
| copper |
-0.1745 |
1.097 |
-0.1591 |
0.8736 |
|
| iron |
-3.867 |
2.732 |
-1.415 |
0.1569 |
|
| manganese |
2.61 |
1.752 |
1.49 |
0.1362 |
|
| mercury |
-0.207 |
0.913 |
-0.2268 |
0.8206 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.72 |
1 |
1.7 |
1 |
2.08 |
1.45 |
2.5 |
4.84 |
4.22 |
2.02 |

Influence indices
## McFadden's pseudo-R2 is: -8.94
## Tjur's pseudo-R2 is: 0.15
## Pearson's pseudo-R2 is: 0.07
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.859e+15 |
7496963 |
-2.48e+08 |
0 |
* * * |
| aquaculture |
-1.65e+15 |
66771996 |
-24705435 |
0 |
* * * |
| city |
1.632e+15 |
60515998 |
26960600 |
0 |
* * * |
| dredging_collect |
2.193e+14 |
47886179 |
4580309 |
0 |
* * * |
| dredging_dump |
-1.273e+14 |
55589796 |
-2289661 |
0 |
* * * |
| industry |
2.026e+15 |
30193191 |
67105166 |
0 |
* * * |
| shipping_mooring |
-1.194e+15 |
51224704 |
-23309623 |
0 |
* * * |
| shipping_traffic |
-9.411e+14 |
22815885 |
-41246884 |
0 |
* * * |
| sewers_rain |
1.998e+15 |
67164046 |
29742764 |
0 |
* * * |
| sewers_waste |
-1.87e+15 |
90359677 |
-20691261 |
0 |
* * * |
| wharves_city |
-2.219e+15 |
72465526 |
-30623705 |
0 |
* * * |
| wharves_industry |
-5.91e+14 |
79067645 |
-7474226 |
0 |
* * * |
| fisheries_trap |
-2.403e+14 |
7277539 |
-33026188 |
0 |
* * * |
| fisheries_trawl |
3.983e+14 |
8821984 |
45152815 |
0 |
* * * |
| fisheries_net |
-2.655e+14 |
7219163 |
-36774956 |
0 |
* * * |
| fisheries_dredge |
-4.199e+14 |
19454028 |
-21586018 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Nephtys caeca
## SDM for: nephtys_caeca
Abiotic parameters
## McFadden's pseudo-R2 is: 0.35
## Tjur's pseudo-R2 is: 0.37
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-373.6 |
58826 |
-0.006351 |
0.9949 |
|
| om |
-0.9548 |
1.092 |
-0.8746 |
0.3818 |
|
| gravel |
0.485 |
0.414 |
1.172 |
0.2413 |
|
| silt |
-0.08846 |
0.9323 |
-0.09489 |
0.9244 |
|
| clay |
-2021 |
321005 |
-0.006295 |
0.995 |
|
| arsenic |
-0.9707 |
1.413 |
-0.6872 |
0.492 |
|
| cadmium |
1.709 |
0.7253 |
2.357 |
0.01842 |
* |
| copper |
-0.09584 |
0.8005 |
-0.1197 |
0.9047 |
|
| iron |
1.088 |
0.5293 |
2.056 |
0.03978 |
* |
| manganese |
-0.5698 |
1.018 |
-0.5598 |
0.5756 |
|
| mercury |
-0.7142 |
1.121 |
-0.6369 |
0.5242 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.99 |
1.34 |
1.88 |
1 |
2.09 |
1.99 |
1.99 |
1.63 |
1.54 |
1.6 |

Influence indices
## McFadden's pseudo-R2 is: 0.54
## Tjur's pseudo-R2 is: 0.45
## Pearson's pseudo-R2 is: 0.43
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-10.18 |
22.7 |
-0.4485 |
0.6538 |
|
| aquaculture |
21.39 |
11.2 |
1.91 |
0.05608 |
|
| city |
6.18 |
5.218 |
1.184 |
0.2363 |
|
| dredging_collect |
-9.698 |
8.327 |
-1.165 |
0.2442 |
|
| dredging_dump |
3.272 |
6.1 |
0.5363 |
0.5918 |
|
| industry |
-14.47 |
6.897 |
-2.098 |
0.03589 |
* |
| shipping_mooring |
9.344 |
5.286 |
1.768 |
0.07712 |
|
| shipping_traffic |
-0.8441 |
2.157 |
-0.3914 |
0.6955 |
|
| sewers_rain |
-13.46 |
9.76 |
-1.379 |
0.1678 |
|
| sewers_waste |
11.36 |
13.59 |
0.8364 |
0.4029 |
|
| wharves_city |
-12.11 |
7.535 |
-1.607 |
0.1081 |
|
| wharves_industry |
19.01 |
11.79 |
1.612 |
0.1069 |
|
| fisheries_trap |
1.024 |
0.4634 |
2.21 |
0.02712 |
* |
| fisheries_trawl |
-0.7393 |
1.216 |
-0.6081 |
0.5431 |
|
| fisheries_net |
-0.6575 |
230.6 |
-0.002851 |
0.9977 |
|
| fisheries_dredge |
-20.53 |
11.75 |
-1.747 |
0.08062 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
21.1 |
13.2 |
19.7 |
14.6 |
13 |
9.93 |
4.32 |
14.6 |
21.7 |
20.4 |
26.7 |
1.67 |
2.75 |
1 |
3.41 |

Nephtys incisa
## SDM for: nephtys_incisa
Abiotic parameters
## McFadden's pseudo-R2 is: 0.26
## Tjur's pseudo-R2 is: 0.29
## Pearson's pseudo-R2 is: 0.29
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.318 |
0.4156 |
-3.17 |
0.001524 |
* * |
| om |
1.799 |
0.6481 |
2.776 |
0.005495 |
* * |
| gravel |
0.1471 |
0.3153 |
0.4664 |
0.6409 |
|
| silt |
-0.04529 |
0.601 |
-0.07535 |
0.9399 |
|
| clay |
-0.0824 |
1.533 |
-0.05375 |
0.9571 |
|
| arsenic |
0.75 |
0.4802 |
1.562 |
0.1183 |
|
| cadmium |
-1.761 |
0.7329 |
-2.403 |
0.01624 |
* |
| copper |
0.2368 |
0.7444 |
0.3181 |
0.7504 |
|
| iron |
-0.9342 |
0.7163 |
-1.304 |
0.1921 |
|
| manganese |
-0.8585 |
0.7242 |
-1.186 |
0.2358 |
|
| mercury |
0.3689 |
0.5156 |
0.7155 |
0.4743 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.43 |
1.44 |
2.24 |
1.05 |
1.68 |
2.32 |
2.62 |
2.06 |
2.45 |
1.82 |

Influence indices
## McFadden's pseudo-R2 is: 0.32
## Tjur's pseudo-R2 is: 0.32
## Pearson's pseudo-R2 is: 0.32
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-73.61 |
6436 |
-0.01144 |
0.9909 |
|
| aquaculture |
-1.622 |
2.674 |
-0.6065 |
0.5442 |
|
| city |
-26.86 |
14.13 |
-1.902 |
0.05722 |
|
| dredging_collect |
6.129 |
2.863 |
2.141 |
0.03228 |
* |
| dredging_dump |
3.482 |
3.398 |
1.025 |
0.3055 |
|
| industry |
1.757 |
1.268 |
1.385 |
0.1661 |
|
| shipping_mooring |
9.422 |
5.379 |
1.752 |
0.07981 |
|
| shipping_traffic |
-0.7808 |
1.453 |
-0.5376 |
0.5909 |
|
| sewers_rain |
9.013 |
4.595 |
1.962 |
0.04981 |
* |
| sewers_waste |
-16.2 |
6.962 |
-2.327 |
0.01999 |
* |
| wharves_city |
19.94 |
11.41 |
1.747 |
0.0807 |
|
| wharves_industry |
-9.671 |
5.031 |
-1.922 |
0.05457 |
|
| fisheries_trap |
0.1523 |
0.494 |
0.3083 |
0.7579 |
|
| fisheries_trawl |
0.01495 |
0.3058 |
0.04889 |
0.961 |
|
| fisheries_net |
-724.7 |
66734 |
-0.01086 |
0.9913 |
|
| fisheries_dredge |
-0.6645 |
0.6645 |
-1 |
0.3173 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.2 |
39.5 |
8.86 |
10.7 |
2.73 |
20.3 |
4.68 |
16.6 |
26.8 |
34.2 |
15.6 |
1.15 |
1.4 |
1 |
1.72 |

Nephtys sp
## SDM for: nephtys_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.09
## Tjur's pseudo-R2 is: 0.01
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-124.5 |
25087 |
-0.004963 |
0.996 |
|
| om |
-0.3531 |
1.404 |
-0.2514 |
0.8015 |
|
| gravel |
-31.13 |
19188 |
-0.001622 |
0.9987 |
|
| silt |
0.2853 |
1.383 |
0.2064 |
0.8365 |
|
| clay |
-611.9 |
133753 |
-0.004575 |
0.9963 |
|
| arsenic |
0.2589 |
0.9203 |
0.2813 |
0.7785 |
|
| cadmium |
-0.1391 |
1.244 |
-0.1118 |
0.911 |
|
| copper |
0.6144 |
1.615 |
0.3804 |
0.7037 |
|
| iron |
-0.8727 |
2.341 |
-0.3728 |
0.7093 |
|
| manganese |
-0.2432 |
1.998 |
-0.1217 |
0.9031 |
|
| mercury |
-0.2722 |
1.452 |
-0.1875 |
0.8512 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.61 |
1 |
1.63 |
1 |
1.46 |
1.6 |
2.26 |
2.54 |
2.1 |
1.54 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-185 |
498200 |
-0.0003713 |
0.9997 |
|
| aquaculture |
409.2 |
20784553 |
1.969e-05 |
1 |
|
| city |
-253.2 |
26358717 |
-9.605e-06 |
1 |
|
| dredging_collect |
-3.953 |
7931459 |
-4.983e-07 |
1 |
|
| dredging_dump |
-60.55 |
5450091 |
-1.111e-05 |
1 |
|
| industry |
67.95 |
4340815 |
1.565e-05 |
1 |
|
| shipping_mooring |
196.7 |
13179186 |
1.493e-05 |
1 |
|
| shipping_traffic |
-37.57 |
2250903 |
-1.669e-05 |
1 |
|
| sewers_rain |
-86.76 |
3594033 |
-2.414e-05 |
1 |
|
| sewers_waste |
353.4 |
10172112 |
3.474e-05 |
1 |
|
| wharves_city |
283.6 |
26524927 |
1.069e-05 |
1 |
|
| wharves_industry |
-159.2 |
4820664 |
-3.303e-05 |
1 |
|
| fisheries_trap |
-6.987 |
2822742 |
-2.475e-06 |
1 |
|
| fisheries_trawl |
-10.63 |
494830 |
-2.148e-05 |
1 |
|
| fisheries_net |
1.548 |
266450 |
5.808e-06 |
1 |
|
| fisheries_dredge |
-133.7 |
2704897 |
-4.943e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
358 |
187 |
56.8 |
36 |
44.1 |
243 |
23.5 |
49.1 |
154 |
184 |
32.6 |
9.03 |
3.18 |
1.06 |
7.81 |

Nuculana minuta
## SDM for: nuculana_minuta
Abiotic parameters
## McFadden's pseudo-R2 is: 0.16
## Tjur's pseudo-R2 is: 0.07
## Pearson's pseudo-R2 is: 0.05
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-324.9 |
50727 |
-0.006405 |
0.9949 |
|
| om |
0.4017 |
0.8142 |
0.4933 |
0.6218 |
|
| gravel |
0.2555 |
0.388 |
0.6586 |
0.5102 |
|
| silt |
0.4505 |
0.8164 |
0.5518 |
0.5811 |
|
| clay |
-1758 |
276808 |
-0.00635 |
0.9949 |
|
| arsenic |
-0.7582 |
1.962 |
-0.3864 |
0.6992 |
|
| cadmium |
-0.518 |
0.7624 |
-0.6794 |
0.4969 |
|
| copper |
-0.1736 |
1.005 |
-0.1727 |
0.8629 |
|
| iron |
-0.6751 |
1.007 |
-0.6703 |
0.5027 |
|
| manganese |
0.3039 |
1.439 |
0.2113 |
0.8327 |
|
| mercury |
-0.5311 |
0.9704 |
-0.5473 |
0.5842 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.91 |
1.32 |
2.08 |
1 |
1.92 |
1.53 |
1.89 |
1.85 |
2.46 |
1.69 |

Influence indices
## McFadden's pseudo-R2 is: -4.42
## Tjur's pseudo-R2 is: 0.61
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.432e+15 |
7496963 |
-191056064 |
0 |
* * * |
| aquaculture |
4.56e+15 |
66771996 |
68298016 |
0 |
* * * |
| city |
2.319e+15 |
60515998 |
38328191 |
0 |
* * * |
| dredging_collect |
5.398e+15 |
47886179 |
112725040 |
0 |
* * * |
| dredging_dump |
1.47e+15 |
55589796 |
26451871 |
0 |
* * * |
| industry |
-1.161e+15 |
30193191 |
-38447880 |
0 |
* * * |
| shipping_mooring |
5.288e+15 |
51224704 |
103233248 |
0 |
* * * |
| shipping_traffic |
1.002e+15 |
22815885 |
43908825 |
0 |
* * * |
| sewers_rain |
2.122e+15 |
67164046 |
31596831 |
0 |
* * * |
| sewers_waste |
-2.1e+15 |
90359677 |
-23235378 |
0 |
* * * |
| wharves_city |
-5.124e+15 |
72465526 |
-70711503 |
0 |
* * * |
| wharves_industry |
-7.384e+15 |
79067645 |
-93385449 |
0 |
* * * |
| fisheries_trap |
-4.244e+14 |
7277539 |
-58316903 |
0 |
* * * |
| fisheries_trawl |
-1.541e+14 |
8821984 |
-17470949 |
0 |
* * * |
| fisheries_net |
3.806e+14 |
7219163 |
52727565 |
0 |
* * * |
| fisheries_dredge |
7.068e+14 |
19454028 |
36330369 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Nymphonidae spp
## SDM for: nymphonidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-95.8 |
274909 |
-0.0003485 |
0.9997 |
|
| om |
-10.42 |
524836 |
-1.985e-05 |
1 |
|
| gravel |
6.602 |
77488 |
8.52e-05 |
0.9999 |
|
| silt |
-1.753 |
229514 |
-7.637e-06 |
1 |
|
| clay |
23.84 |
1153878 |
2.066e-05 |
1 |
|
| arsenic |
-107.1 |
345443 |
-0.0003101 |
0.9998 |
|
| cadmium |
-11.83 |
187356 |
-6.316e-05 |
0.9999 |
|
| copper |
7.065 |
286431 |
2.467e-05 |
1 |
|
| iron |
-26.82 |
273093 |
-9.822e-05 |
0.9999 |
|
| manganese |
42.82 |
545233 |
7.854e-05 |
0.9999 |
|
| mercury |
10.22 |
290476 |
3.52e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.62 |
2.62 |
3.86 |
2 |
1.76 |
5.63 |
3.51 |
3.92 |
8.2 |
3.55 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-116.6 |
240165 |
-0.0004857 |
0.9996 |
|
| aquaculture |
-168.3 |
1102516 |
-0.0001526 |
0.9999 |
|
| city |
251.4 |
1289120 |
0.000195 |
0.9998 |
|
| dredging_collect |
9.64 |
1661898 |
5.801e-06 |
1 |
|
| dredging_dump |
151.3 |
1022875 |
0.000148 |
0.9999 |
|
| industry |
-34.59 |
566055 |
-6.111e-05 |
1 |
|
| shipping_mooring |
2.434 |
869643 |
2.799e-06 |
1 |
|
| shipping_traffic |
-48.51 |
852461 |
-5.69e-05 |
1 |
|
| sewers_rain |
332.8 |
1136950 |
0.0002927 |
0.9998 |
|
| sewers_waste |
-562 |
1673553 |
-0.0003358 |
0.9997 |
|
| wharves_city |
-312.7 |
1361612 |
-0.0002297 |
0.9998 |
|
| wharves_industry |
27.32 |
2040076 |
1.339e-05 |
1 |
|
| fisheries_trap |
18.7 |
82254 |
0.0002273 |
0.9998 |
|
| fisheries_trawl |
16.11 |
167002 |
9.649e-05 |
0.9999 |
|
| fisheries_net |
3.69 |
162216 |
2.275e-05 |
1 |
|
| fisheries_dredge |
30.81 |
692729 |
4.448e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
12.6 |
20.1 |
19 |
12.9 |
6.88 |
7.74 |
11 |
11.3 |
15.7 |
23.9 |
24.1 |
2.03 |
2 |
1.04 |
6.34 |

Oenopota sp
## SDM for: oenopota_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.32
## Tjur's pseudo-R2 is: 0.27
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-4.727 |
1.254 |
-3.769 |
0.0001638 |
* * * |
| om |
-1.065 |
0.9046 |
-1.178 |
0.2389 |
|
| gravel |
0.8247 |
0.5969 |
1.382 |
0.1671 |
|
| silt |
1.73 |
1.044 |
1.657 |
0.09746 |
|
| clay |
-0.9584 |
2.099 |
-0.4565 |
0.648 |
|
| arsenic |
0.2882 |
1.065 |
0.2706 |
0.7867 |
|
| cadmium |
-0.2272 |
0.9921 |
-0.229 |
0.8188 |
|
| copper |
1.853 |
1.417 |
1.308 |
0.1909 |
|
| iron |
-5.839 |
2.913 |
-2.005 |
0.04502 |
* |
| manganese |
3.167 |
1.721 |
1.84 |
0.06581 |
|
| mercury |
-1.661 |
1.068 |
-1.555 |
0.1199 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.76 |
1.58 |
2.29 |
1.06 |
2.43 |
1.92 |
3.37 |
5.23 |
4.36 |
1.75 |

Influence indices
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.16
## Pearson's pseudo-R2 is: 0.14
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-556.1 |
118321 |
-0.0047 |
0.9963 |
|
| aquaculture |
6.541 |
8.578 |
0.7625 |
0.4458 |
|
| city |
10.11 |
7.582 |
1.333 |
0.1826 |
|
| dredging_collect |
-5.998 |
6.758 |
-0.8876 |
0.3747 |
|
| dredging_dump |
-0.8892 |
5.019 |
-0.1772 |
0.8594 |
|
| industry |
2.124 |
2.58 |
0.8231 |
0.4104 |
|
| shipping_mooring |
2.109 |
6.197 |
0.3404 |
0.7336 |
|
| shipping_traffic |
-1.708 |
4.294 |
-0.3978 |
0.6908 |
|
| sewers_rain |
-1.5 |
5.9 |
-0.2543 |
0.7993 |
|
| sewers_waste |
4.891 |
8.401 |
0.5822 |
0.5604 |
|
| wharves_city |
-9.068 |
8.347 |
-1.086 |
0.2773 |
|
| wharves_industry |
7.18 |
9.081 |
0.7907 |
0.4291 |
|
| fisheries_trap |
0.5012 |
0.6399 |
0.7832 |
0.4335 |
|
| fisheries_trawl |
-1845 |
320648 |
-0.005754 |
0.9954 |
|
| fisheries_net |
-651.4 |
852238 |
-0.0007643 |
0.9994 |
|
| fisheries_dredge |
2.187 |
2.412 |
0.9069 |
0.3645 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
16.6 |
15.7 |
14.8 |
12.2 |
7.99 |
11.1 |
12.4 |
12 |
18.1 |
15.7 |
22.6 |
1.59 |
1 |
1 |
2.32 |

Oligochaeta
## SDM for: oligochaeta
Abiotic parameters
## McFadden's pseudo-R2 is: 0.36
## Tjur's pseudo-R2 is: 0.32
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-109.9 |
15471 |
-0.007102 |
0.9943 |
|
| om |
0.8845 |
0.7413 |
1.193 |
0.2328 |
|
| gravel |
-28.92 |
9547 |
-0.003029 |
0.9976 |
|
| silt |
0.1045 |
1.069 |
0.09777 |
0.9221 |
|
| clay |
-541.4 |
83177 |
-0.006509 |
0.9948 |
|
| arsenic |
-2.147 |
1.357 |
-1.583 |
0.1135 |
|
| cadmium |
0.5475 |
0.6447 |
0.8493 |
0.3957 |
|
| copper |
0.6134 |
0.7771 |
0.7894 |
0.4299 |
|
| iron |
0.03174 |
1.053 |
0.03013 |
0.976 |
|
| manganese |
-0.2085 |
0.8471 |
-0.2462 |
0.8055 |
|
| mercury |
1.166 |
0.694 |
1.681 |
0.09286 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.88 |
1 |
2.12 |
1 |
2 |
1.37 |
1.7 |
1.89 |
1.84 |
1.59 |

Influence indices
## McFadden's pseudo-R2 is: -10.41
## Tjur's pseudo-R2 is: 0.43
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.458e+15 |
7496963 |
-327858358 |
0 |
* * * |
| aquaculture |
-3.795e+15 |
66771996 |
-56833032 |
0 |
* * * |
| city |
-1.047e+15 |
60515998 |
-17296953 |
0 |
* * * |
| dredging_collect |
-3.77e+14 |
47886179 |
-7873313 |
0 |
* * * |
| dredging_dump |
1.842e+15 |
55589796 |
33132528 |
0 |
* * * |
| industry |
5.258e+14 |
30193191 |
17414925 |
0 |
* * * |
| shipping_mooring |
-1.942e+15 |
51224704 |
-37903638 |
0 |
* * * |
| shipping_traffic |
8.97e+14 |
22815885 |
39314681 |
0 |
* * * |
| sewers_rain |
-3.478e+15 |
67164046 |
-51785502 |
0 |
* * * |
| sewers_waste |
2.334e+15 |
90359677 |
25825931 |
0 |
* * * |
| wharves_city |
2.102e+15 |
72465526 |
29005272 |
0 |
* * * |
| wharves_industry |
-1.521e+15 |
79067645 |
-19237755 |
0 |
* * * |
| fisheries_trap |
-6.339e+13 |
7277539 |
-8710477 |
0 |
* * * |
| fisheries_trawl |
2.722e+14 |
8821984 |
30855581 |
0 |
* * * |
| fisheries_net |
2.094e+14 |
7219163 |
29005761 |
0 |
* * * |
| fisheries_dredge |
1.608e+14 |
19454028 |
8265185 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Ophelia limacina
## SDM for: ophelia_limacina
Abiotic parameters
## McFadden's pseudo-R2 is: 0.17
## Tjur's pseudo-R2 is: 0.11
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-363.9 |
59835 |
-0.006082 |
0.9951 |
|
| om |
-1.227 |
1.128 |
-1.088 |
0.2766 |
|
| gravel |
0.6418 |
0.4589 |
1.399 |
0.162 |
|
| silt |
0.42 |
0.9173 |
0.4578 |
0.6471 |
|
| clay |
-1966 |
326512 |
-0.006023 |
0.9952 |
|
| arsenic |
0.302 |
0.8228 |
0.367 |
0.7136 |
|
| cadmium |
0.03719 |
1.043 |
0.03566 |
0.9716 |
|
| copper |
0.7528 |
1.378 |
0.5462 |
0.5849 |
|
| iron |
0.01492 |
1.618 |
0.009225 |
0.9926 |
|
| manganese |
-1.309 |
1.266 |
-1.034 |
0.3013 |
|
| mercury |
0.7863 |
0.7895 |
0.996 |
0.3192 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.86 |
1.58 |
1.88 |
1 |
1.57 |
1.98 |
2.81 |
2.38 |
1.93 |
1.63 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-158.9 |
249610 |
-0.0006365 |
0.9995 |
|
| aquaculture |
405.9 |
1950174 |
0.0002081 |
0.9998 |
|
| city |
301.4 |
2840020 |
0.0001061 |
0.9999 |
|
| dredging_collect |
135.7 |
959463 |
0.0001414 |
0.9999 |
|
| dredging_dump |
5.799 |
575818 |
1.007e-05 |
1 |
|
| industry |
-74.11 |
421515 |
-0.0001758 |
0.9999 |
|
| shipping_mooring |
274.5 |
1052677 |
0.0002608 |
0.9998 |
|
| shipping_traffic |
111.3 |
519451 |
0.0002143 |
0.9998 |
|
| sewers_rain |
-117.6 |
2010549 |
-5.851e-05 |
1 |
|
| sewers_waste |
150.3 |
2163606 |
6.946e-05 |
0.9999 |
|
| wharves_city |
-390.6 |
2725877 |
-0.0001433 |
0.9999 |
|
| wharves_industry |
-201 |
1326411 |
-0.0001515 |
0.9999 |
|
| fisheries_trap |
-119.9 |
978061 |
-0.0001226 |
0.9999 |
|
| fisheries_trawl |
-35.69 |
137617 |
-0.0002594 |
0.9998 |
|
| fisheries_net |
4.749 |
225552 |
2.106e-05 |
1 |
|
| fisheries_dredge |
-224.5 |
714188 |
-0.0003143 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
48.1 |
50.9 |
20 |
10.7 |
8.01 |
29.2 |
9.84 |
40 |
46.4 |
48.4 |
25.6 |
5.92 |
3.98 |
1.47 |
2.7 |

Opheliidae spp
## SDM for: opheliidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.6
## Tjur's pseudo-R2 is: 0.41
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-135.2 |
121671 |
-0.001111 |
0.9991 |
|
| om |
-1.282 |
4.632 |
-0.2768 |
0.782 |
|
| gravel |
2.394 |
3.544 |
0.6755 |
0.4993 |
|
| silt |
4.39 |
9.869 |
0.4448 |
0.6564 |
|
| clay |
-641.9 |
663941 |
-0.0009669 |
0.9992 |
|
| arsenic |
4.284 |
6.077 |
0.7051 |
0.4808 |
|
| cadmium |
3.629 |
6.339 |
0.5726 |
0.5669 |
|
| copper |
-2.033 |
8.999 |
-0.2259 |
0.8213 |
|
| iron |
1.842 |
3.289 |
0.5599 |
0.5755 |
|
| manganese |
-11.17 |
26.08 |
-0.4282 |
0.6685 |
|
| mercury |
-10.87 |
9.945 |
-1.093 |
0.2745 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.01 |
7.87 |
10.5 |
1 |
3.57 |
5 |
5.13 |
3.42 |
6.3 |
3.64 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-452.8 |
1024570 |
-0.0004419 |
0.9996 |
|
| aquaculture |
-224.8 |
2536378 |
-8.862e-05 |
0.9999 |
|
| city |
-997.3 |
4150230 |
-0.0002403 |
0.9998 |
|
| dredging_collect |
-1036 |
4226919 |
-0.000245 |
0.9998 |
|
| dredging_dump |
-439.8 |
4577201 |
-9.609e-05 |
0.9999 |
|
| industry |
298.1 |
1843809 |
0.0001617 |
0.9999 |
|
| shipping_mooring |
-1147 |
4142139 |
-0.0002769 |
0.9998 |
|
| shipping_traffic |
-169.8 |
500126 |
-0.0003395 |
0.9997 |
|
| sewers_rain |
-1299 |
5298398 |
-0.0002451 |
0.9998 |
|
| sewers_waste |
2027 |
8653272 |
0.0002343 |
0.9998 |
|
| wharves_city |
1665 |
7533631 |
0.000221 |
0.9998 |
|
| wharves_industry |
1103 |
5599664 |
0.000197 |
0.9998 |
|
| fisheries_trap |
-373.1 |
1986454 |
-0.0001878 |
0.9999 |
|
| fisheries_trawl |
36.4 |
143366 |
0.0002539 |
0.9998 |
|
| fisheries_net |
100.6 |
6462729 |
1.557e-05 |
1 |
|
| fisheries_dredge |
-156.8 |
433587 |
-0.0003617 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
44.9 |
58.9 |
82 |
83.8 |
32 |
67.9 |
10.3 |
102 |
172 |
109 |
106 |
3.36 |
4.8 |
1 |
5.91 |

Ophiopholis aculeata
## SDM for: ophiopholis_aculeata
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-381.8 |
929534 |
-0.0004108 |
0.9997 |
|
| om |
-166.7 |
428880 |
-0.0003886 |
0.9997 |
|
| gravel |
19.89 |
290335 |
6.851e-05 |
0.9999 |
|
| silt |
171.2 |
433268 |
0.0003951 |
0.9997 |
|
| clay |
-728.3 |
2170437 |
-0.0003355 |
0.9997 |
|
| arsenic |
38.32 |
138267 |
0.0002771 |
0.9998 |
|
| cadmium |
-91.9 |
301965 |
-0.0003043 |
0.9998 |
|
| copper |
38.28 |
179382 |
0.0002134 |
0.9998 |
|
| iron |
1.822 |
89274 |
2.041e-05 |
1 |
|
| manganese |
66.61 |
351870 |
0.0001893 |
0.9998 |
|
| mercury |
-206.6 |
565129 |
-0.0003656 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.54 |
1.07 |
6.94 |
1.69 |
1.86 |
3.36 |
1.95 |
1.61 |
2.4 |
5.19 |

Influence indices
## McFadden's pseudo-R2 is: -2.79
## Tjur's pseudo-R2 is: 0.5
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.799e+15 |
7496963 |
-373354674 |
0 |
* * * |
| aquaculture |
2.674e+15 |
66771996 |
40044326 |
0 |
* * * |
| city |
-4.437e+14 |
60515998 |
-7332363 |
0 |
* * * |
| dredging_collect |
2.276e+15 |
47886179 |
47539070 |
0 |
* * * |
| dredging_dump |
3.273e+15 |
55589796 |
58870875 |
0 |
* * * |
| industry |
1.1e+15 |
30193191 |
36425419 |
0 |
* * * |
| shipping_mooring |
2.533e+15 |
51224704 |
49448042 |
0 |
* * * |
| shipping_traffic |
-2.486e+15 |
22815885 |
-108944284 |
0 |
* * * |
| sewers_rain |
3.584e+15 |
67164046 |
53358494 |
0 |
* * * |
| sewers_waste |
-1.608e+15 |
90359677 |
-17797607 |
0 |
* * * |
| wharves_city |
-1.468e+15 |
72465526 |
-20259531 |
0 |
* * * |
| wharves_industry |
-5.806e+15 |
79067645 |
-73434045 |
0 |
* * * |
| fisheries_trap |
1.199e+14 |
7277539 |
16473947 |
0 |
* * * |
| fisheries_trawl |
4.707e+14 |
8821984 |
53355115 |
0 |
* * * |
| fisheries_net |
1.527e+14 |
7219163 |
21145155 |
0 |
* * * |
| fisheries_dredge |
-8.88e+14 |
19454028 |
-45646005 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Ophiura robusta
## SDM for: ophiura_robusta
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1451 |
2311942 |
-0.0006275 |
0.9995 |
|
| om |
-896 |
1448991 |
-0.0006183 |
0.9995 |
|
| gravel |
-28.47 |
108848 |
-0.0002616 |
0.9998 |
|
| silt |
-3.358 |
225083 |
-1.492e-05 |
1 |
|
| clay |
310.9 |
618796 |
0.0005024 |
0.9996 |
|
| arsenic |
-324.2 |
1219691 |
-0.0002658 |
0.9998 |
|
| cadmium |
-168 |
373256 |
-0.00045 |
0.9996 |
|
| copper |
243.7 |
426199 |
0.0005717 |
0.9995 |
|
| iron |
154.6 |
286481 |
0.0005396 |
0.9996 |
|
| manganese |
-671 |
1230015 |
-0.0005456 |
0.9996 |
|
| mercury |
-12.74 |
372098 |
-3.425e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
5.32 |
3.22 |
3.41 |
1.89 |
2.99 |
4.47 |
6.59 |
4.74 |
7.2 |
3.77 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-377.9 |
1006500 |
-0.0003755 |
0.9997 |
|
| aquaculture |
-368.3 |
1523576 |
-0.0002417 |
0.9998 |
|
| city |
791.8 |
2586677 |
0.0003061 |
0.9998 |
|
| dredging_collect |
267.7 |
1213713 |
0.0002206 |
0.9998 |
|
| dredging_dump |
646 |
2262021 |
0.0002856 |
0.9998 |
|
| industry |
-233.5 |
787343 |
-0.0002965 |
0.9998 |
|
| shipping_mooring |
65.96 |
1124570 |
5.865e-05 |
1 |
|
| shipping_traffic |
-130.6 |
531926 |
-0.0002455 |
0.9998 |
|
| sewers_rain |
1156 |
3052363 |
0.0003788 |
0.9997 |
|
| sewers_waste |
-2038 |
5104173 |
-0.0003993 |
0.9997 |
|
| wharves_city |
-1135 |
3672102 |
-0.000309 |
0.9998 |
|
| wharves_industry |
-252.1 |
1745371 |
-0.0001444 |
0.9999 |
|
| fisheries_trap |
24.99 |
309605 |
8.071e-05 |
0.9999 |
|
| fisheries_trawl |
7.673 |
63749 |
0.0001204 |
0.9999 |
|
| fisheries_net |
-1.589 |
6466823 |
-2.457e-07 |
1 |
|
| fisheries_dredge |
-333.7 |
1013326 |
-0.0003293 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.3 |
38.3 |
17.7 |
34.2 |
8.88 |
15.2 |
9.88 |
52.2 |
67 |
56.5 |
26.3 |
1.87 |
2.87 |
1.12 |
5.5 |

Orchomenella minuta
## SDM for: orchomenella_minuta
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-96.53 |
269243 |
-0.0003585 |
0.9997 |
|
| om |
41.74 |
341110 |
0.0001224 |
0.9999 |
|
| gravel |
5.826 |
210168 |
2.772e-05 |
1 |
|
| silt |
24.89 |
423176 |
5.881e-05 |
1 |
|
| clay |
-20.02 |
1126809 |
-1.777e-05 |
1 |
|
| arsenic |
-16.27 |
660117 |
-2.465e-05 |
1 |
|
| cadmium |
-61.42 |
304738 |
-0.0002015 |
0.9998 |
|
| copper |
34.47 |
333143 |
0.0001035 |
0.9999 |
|
| iron |
0.4691 |
412637 |
1.137e-06 |
1 |
|
| manganese |
-29.78 |
289510 |
-0.0001029 |
0.9999 |
|
| mercury |
17.92 |
320937 |
5.585e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
5.81 |
2.22 |
4.48 |
1.89 |
6.37 |
6.94 |
8.24 |
6.33 |
4.26 |
5.52 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-47.74 |
121771 |
-0.000392 |
0.9997 |
|
| aquaculture |
-66.67 |
2475590 |
-2.693e-05 |
1 |
|
| city |
-35.92 |
960406 |
-3.741e-05 |
1 |
|
| dredging_collect |
71.99 |
1559178 |
4.617e-05 |
1 |
|
| dredging_dump |
139.4 |
1455965 |
9.576e-05 |
0.9999 |
|
| industry |
37.12 |
953112 |
3.895e-05 |
1 |
|
| shipping_mooring |
20.65 |
2689759 |
7.679e-06 |
1 |
|
| shipping_traffic |
-33.48 |
725208 |
-4.617e-05 |
1 |
|
| sewers_rain |
177.6 |
1377968 |
0.0001289 |
0.9999 |
|
| sewers_waste |
-208.7 |
1842401 |
-0.0001133 |
0.9999 |
|
| wharves_city |
-5.049 |
1100192 |
-4.589e-06 |
1 |
|
| wharves_industry |
-216.8 |
1689674 |
-0.0001283 |
0.9999 |
|
| fisheries_trap |
1.871 |
392615 |
4.765e-06 |
1 |
|
| fisheries_trawl |
-5.469 |
493752 |
-1.108e-05 |
1 |
|
| fisheries_net |
-3.411 |
106286 |
-3.209e-05 |
1 |
|
| fisheries_dredge |
-3.425 |
458731 |
-7.466e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
26.1 |
11.4 |
17.1 |
16.6 |
9.62 |
30.8 |
9 |
21.5 |
21 |
13.2 |
19.2 |
1.89 |
4.57 |
1.08 |
4.8 |

Ostracoda
## SDM for: ostracoda
Abiotic parameters
## McFadden's pseudo-R2 is: 0.2
## Tjur's pseudo-R2 is: 0.21
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.77 |
0.5656 |
-3.13 |
0.001748 |
* * |
| om |
0.2179 |
0.6015 |
0.3623 |
0.7171 |
|
| gravel |
-0.7979 |
0.7211 |
-1.107 |
0.2685 |
|
| silt |
0.196 |
0.5838 |
0.3357 |
0.7371 |
|
| clay |
-1.496 |
2.496 |
-0.5992 |
0.5491 |
|
| arsenic |
0.7567 |
0.5011 |
1.51 |
0.131 |
|
| cadmium |
-0.9634 |
0.6149 |
-1.567 |
0.1171 |
|
| copper |
0.9222 |
0.7102 |
1.299 |
0.1941 |
|
| iron |
-0.9031 |
0.8186 |
-1.103 |
0.2699 |
|
| manganese |
-1.228 |
0.8245 |
-1.49 |
0.1363 |
|
| mercury |
-0.2323 |
0.5896 |
-0.394 |
0.6936 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.94 |
1.11 |
1.89 |
1.05 |
1.66 |
1.95 |
2.54 |
2.2 |
2.05 |
1.64 |

Influence indices
## McFadden's pseudo-R2 is: 0.26
## Tjur's pseudo-R2 is: 0.27
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-48.67 |
5821 |
-0.008361 |
0.9933 |
|
| aquaculture |
3.728 |
3.769 |
0.989 |
0.3226 |
|
| city |
3.856 |
3.893 |
0.9904 |
0.322 |
|
| dredging_collect |
5.293 |
2.39 |
2.215 |
0.02678 |
* |
| dredging_dump |
1.734 |
2.836 |
0.6115 |
0.5408 |
|
| industry |
0.06059 |
1.338 |
0.04527 |
0.9639 |
|
| shipping_mooring |
3.343 |
2.69 |
1.243 |
0.214 |
|
| shipping_traffic |
2.07 |
1.197 |
1.729 |
0.08389 |
|
| sewers_rain |
-0.7488 |
3.063 |
-0.2445 |
0.8069 |
|
| sewers_waste |
0.6046 |
4.474 |
0.1351 |
0.8925 |
|
| wharves_city |
-4.582 |
4.529 |
-1.012 |
0.3116 |
|
| wharves_industry |
-8.502 |
4.386 |
-1.938 |
0.05259 |
|
| fisheries_trap |
-1.141 |
1.197 |
-0.9534 |
0.3404 |
|
| fisheries_trawl |
-0.1146 |
0.3502 |
-0.3272 |
0.7436 |
|
| fisheries_net |
-487.8 |
60354 |
-0.008083 |
0.9936 |
|
| fisheries_dredge |
1.039 |
1.081 |
0.961 |
0.3365 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
12.8 |
13.3 |
8.17 |
9.81 |
4.13 |
9.62 |
4.16 |
10.3 |
15.7 |
15.2 |
14.8 |
1.08 |
1.47 |
1 |
2.01 |

Pagurus sp
## SDM for: pagurus_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-157.4 |
448856 |
-0.0003506 |
0.9997 |
|
| om |
-9.446 |
244648 |
-3.861e-05 |
1 |
|
| gravel |
1.884 |
549794 |
3.427e-06 |
1 |
|
| silt |
25.73 |
120585 |
0.0002134 |
0.9998 |
|
| clay |
3.612 |
2041819 |
1.769e-06 |
1 |
|
| arsenic |
-99.38 |
527677 |
-0.0001883 |
0.9998 |
|
| cadmium |
46.64 |
389449 |
0.0001198 |
0.9999 |
|
| copper |
7.77 |
320737 |
2.423e-05 |
1 |
|
| iron |
5.156 |
458233 |
1.125e-05 |
1 |
|
| manganese |
-96.32 |
760707 |
-0.0001266 |
0.9999 |
|
| mercury |
-33.49 |
301617 |
-0.000111 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.49 |
2.61 |
2.07 |
1.42 |
1.71 |
5.8 |
3.83 |
4.91 |
6.15 |
2.97 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-132 |
261580 |
-0.0005048 |
0.9996 |
|
| aquaculture |
29.9 |
2012429 |
1.486e-05 |
1 |
|
| city |
-44.81 |
2524621 |
-1.775e-05 |
1 |
|
| dredging_collect |
-159.7 |
1088442 |
-0.0001467 |
0.9999 |
|
| dredging_dump |
-24.51 |
2547376 |
-9.621e-06 |
1 |
|
| industry |
-32.52 |
1014058 |
-3.207e-05 |
1 |
|
| shipping_mooring |
-1.868 |
1479903 |
-1.262e-06 |
1 |
|
| shipping_traffic |
-16.82 |
413635 |
-4.066e-05 |
1 |
|
| sewers_rain |
-9.975 |
1818813 |
-5.484e-06 |
1 |
|
| sewers_waste |
47.74 |
3083842 |
1.548e-05 |
1 |
|
| wharves_city |
64.8 |
3296940 |
1.965e-05 |
1 |
|
| wharves_industry |
194.3 |
1348004 |
0.0001441 |
0.9999 |
|
| fisheries_trap |
-166.4 |
673787 |
-0.0002469 |
0.9998 |
|
| fisheries_trawl |
-108.3 |
748721 |
-0.0001446 |
0.9999 |
|
| fisheries_net |
5.202 |
183416 |
2.836e-05 |
1 |
|
| fisheries_dredge |
51.23 |
177084 |
0.0002893 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
30.6 |
29.4 |
14.3 |
34.2 |
9.02 |
22.4 |
6.04 |
31.7 |
53.2 |
36.3 |
18.3 |
2.25 |
2.44 |
1.2 |
2.14 |

Pagurus pubescens
## SDM for: pagurus_pubescens
Abiotic parameters
## McFadden's pseudo-R2 is: 0.7
## Tjur's pseudo-R2 is: 0.6
## Pearson's pseudo-R2 is: 0.62
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-49.6 |
3350 |
-0.01481 |
0.9882 |
|
| om |
-8.766 |
17.51 |
-0.5005 |
0.6167 |
|
| gravel |
2.332 |
4.374 |
0.5332 |
0.5939 |
|
| silt |
-3.38 |
11.27 |
-0.2998 |
0.7643 |
|
| clay |
-9.061 |
18278 |
-0.0004957 |
0.9996 |
|
| arsenic |
-47.11 |
62.46 |
-0.7542 |
0.4507 |
|
| cadmium |
-1.851 |
3.984 |
-0.4647 |
0.6422 |
|
| copper |
4.584 |
4.348 |
1.054 |
0.2917 |
|
| iron |
-5.284 |
18.53 |
-0.2851 |
0.7756 |
|
| manganese |
-1.207 |
24.87 |
-0.04854 |
0.9613 |
|
| mercury |
2.492 |
8.335 |
0.299 |
0.7649 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
5.18 |
8.66 |
8.29 |
1 |
9.14 |
2.6 |
2.2 |
10.1 |
6.25 |
3.48 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-231.2 |
436649 |
-0.0005294 |
0.9996 |
|
| aquaculture |
-318.9 |
9243941 |
-3.449e-05 |
1 |
|
| city |
471.4 |
11980087 |
3.935e-05 |
1 |
|
| dredging_collect |
-1.137 |
5256869 |
-2.163e-07 |
1 |
|
| dredging_dump |
178.3 |
5753943 |
3.098e-05 |
1 |
|
| industry |
16.54 |
664212 |
2.49e-05 |
1 |
|
| shipping_mooring |
43.44 |
13366899 |
3.25e-06 |
1 |
|
| shipping_traffic |
60.44 |
2598359 |
2.326e-05 |
1 |
|
| sewers_rain |
607.8 |
6199623 |
9.803e-05 |
0.9999 |
|
| sewers_waste |
-1147 |
5736706 |
-0.0001999 |
0.9998 |
|
| wharves_city |
-637.9 |
8470576 |
-7.531e-05 |
0.9999 |
|
| wharves_industry |
-17.34 |
3063894 |
-5.658e-06 |
1 |
|
| fisheries_trap |
-23.95 |
274194 |
-8.736e-05 |
0.9999 |
|
| fisheries_trawl |
-69.05 |
1028122 |
-6.716e-05 |
0.9999 |
|
| fisheries_net |
3.257 |
262419 |
1.241e-05 |
1 |
|
| fisheries_dredge |
29.58 |
336525 |
8.79e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
163 |
255 |
116 |
122 |
9.54 |
241 |
63.8 |
132 |
95.9 |
192 |
67.1 |
2.62 |
2.5 |
1.03 |
6.26 |

Pandalus montagui
## SDM for: pandalus_montagui
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-35.81 |
73104 |
-0.0004899 |
0.9996 |
|
| om |
-0.1494 |
115788 |
-1.29e-06 |
1 |
|
| gravel |
-5.465 |
78899 |
-6.926e-05 |
0.9999 |
|
| silt |
-0.4603 |
146274 |
-3.147e-06 |
1 |
|
| clay |
2.35 |
175015 |
1.343e-05 |
1 |
|
| arsenic |
-8.518 |
101783 |
-8.369e-05 |
0.9999 |
|
| cadmium |
-11.21 |
67009 |
-0.0001672 |
0.9999 |
|
| copper |
-0.6614 |
247307 |
-2.675e-06 |
1 |
|
| iron |
-2.182 |
111961 |
-1.949e-05 |
1 |
|
| manganese |
15.04 |
143041 |
0.0001052 |
0.9999 |
|
| mercury |
7.348 |
51678 |
0.0001422 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.45 |
1.91 |
3.8 |
1.15 |
1.74 |
1.49 |
5.61 |
2.3 |
4.63 |
1.89 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-107.6 |
278609 |
-0.000386 |
0.9997 |
|
| aquaculture |
229.3 |
6217901 |
3.687e-05 |
1 |
|
| city |
106.2 |
3672577 |
2.891e-05 |
1 |
|
| dredging_collect |
-182 |
4433016 |
-4.106e-05 |
1 |
|
| dredging_dump |
181.1 |
4784276 |
3.786e-05 |
1 |
|
| industry |
-127 |
1393999 |
-9.109e-05 |
0.9999 |
|
| shipping_mooring |
31.66 |
4425802 |
7.154e-06 |
1 |
|
| shipping_traffic |
11.24 |
2550471 |
4.407e-06 |
1 |
|
| sewers_rain |
-38.7 |
4661417 |
-8.302e-06 |
1 |
|
| sewers_waste |
135.6 |
6361117 |
2.132e-05 |
1 |
|
| wharves_city |
-166.9 |
3683401 |
-4.531e-05 |
1 |
|
| wharves_industry |
122.2 |
4224699 |
2.892e-05 |
1 |
|
| fisheries_trap |
0.8093 |
208879 |
3.875e-06 |
1 |
|
| fisheries_trawl |
-17.17 |
1040430 |
-1.651e-05 |
1 |
|
| fisheries_net |
3.291 |
211067 |
1.559e-05 |
1 |
|
| fisheries_dredge |
-29.93 |
2234845 |
-1.339e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
78.2 |
47.5 |
57.7 |
62.1 |
15.4 |
52.6 |
30 |
57 |
90.7 |
49.4 |
55.8 |
1.97 |
7.26 |
1.38 |
13.1 |

Parathyasira equalis
## SDM for: parathyasira_equalis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.4
## Tjur's pseudo-R2 is: 0.09
## Pearson's pseudo-R2 is: 0.06
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-28.33 |
7334 |
-0.003863 |
0.9969 |
|
| om |
2.883 |
8.032 |
0.3589 |
0.7196 |
|
| gravel |
-26.04 |
11468 |
-0.002271 |
0.9982 |
|
| silt |
-1.792 |
4.549 |
-0.3939 |
0.6936 |
|
| clay |
-48.61 |
35981 |
-0.001351 |
0.9989 |
|
| arsenic |
-1.186 |
25.9 |
-0.04579 |
0.9635 |
|
| cadmium |
1.978 |
4.607 |
0.4293 |
0.6677 |
|
| copper |
-0.6456 |
6.62 |
-0.09751 |
0.9223 |
|
| iron |
-2.897 |
11.07 |
-0.2618 |
0.7935 |
|
| manganese |
-4.971 |
18.11 |
-0.2745 |
0.7837 |
|
| mercury |
-3.972 |
8.918 |
-0.4454 |
0.656 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
6.05 |
1 |
4.36 |
1 |
4.03 |
3.02 |
2.08 |
2.48 |
2.39 |
3.25 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-140.7 |
328669 |
-0.0004281 |
0.9997 |
|
| aquaculture |
229.6 |
10937180 |
2.1e-05 |
1 |
|
| city |
273.9 |
8728790 |
3.137e-05 |
1 |
|
| dredging_collect |
-365.4 |
6986522 |
-5.23e-05 |
1 |
|
| dredging_dump |
-153.6 |
3458238 |
-4.441e-05 |
1 |
|
| industry |
-59.61 |
4474646 |
-1.332e-05 |
1 |
|
| shipping_mooring |
167.9 |
8742986 |
1.92e-05 |
1 |
|
| shipping_traffic |
17.97 |
1883919 |
9.537e-06 |
1 |
|
| sewers_rain |
-66.53 |
2693857 |
-2.47e-05 |
1 |
|
| sewers_waste |
177 |
3450483 |
5.128e-05 |
1 |
|
| wharves_city |
-232.7 |
12961639 |
-1.795e-05 |
1 |
|
| wharves_industry |
536.6 |
5483854 |
9.786e-05 |
0.9999 |
|
| fisheries_trap |
-17.42 |
157690 |
-0.0001105 |
0.9999 |
|
| fisheries_trawl |
-21.83 |
446444 |
-4.889e-05 |
1 |
|
| fisheries_net |
11.02 |
206739 |
5.329e-05 |
1 |
|
| fisheries_dredge |
204.7 |
653920 |
0.0003131 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
197 |
115 |
110 |
48.9 |
47 |
149 |
30.4 |
48.4 |
60.6 |
161 |
85.6 |
2.68 |
4.32 |
1.35 |
9.3 |

Parvicardium pinnulatum
## SDM for: parvicardium_pinnulatum
Abiotic parameters
## McFadden's pseudo-R2 is: 0.34
## Tjur's pseudo-R2 is: 0.26
## Pearson's pseudo-R2 is: 0.31
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-6.95 |
2.796 |
-2.486 |
0.01291 |
* |
| om |
-3.917 |
2.201 |
-1.78 |
0.07515 |
|
| gravel |
0.3256 |
0.7491 |
0.4346 |
0.6639 |
|
| silt |
2.566 |
1.309 |
1.96 |
0.04998 |
* |
| clay |
-0.664 |
1.353 |
-0.4907 |
0.6237 |
|
| arsenic |
-0.9137 |
3.273 |
-0.2792 |
0.7801 |
|
| cadmium |
0.6428 |
1.156 |
0.5562 |
0.5781 |
|
| copper |
2.425 |
1.777 |
1.365 |
0.1723 |
|
| iron |
-1.007 |
2.109 |
-0.4775 |
0.633 |
|
| manganese |
-2.056 |
2.701 |
-0.7615 |
0.4464 |
|
| mercury |
-3.863 |
1.898 |
-2.035 |
0.04183 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.99 |
1.27 |
2.08 |
1.16 |
1.55 |
1.77 |
2.44 |
2.38 |
1.95 |
1.76 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2498 |
3082950 |
-0.0008102 |
0.9994 |
|
| aquaculture |
-230.8 |
2785760 |
-8.285e-05 |
0.9999 |
|
| city |
-2731 |
5314073 |
-0.0005139 |
0.9996 |
|
| dredging_collect |
-1289 |
5058110 |
-0.0002549 |
0.9998 |
|
| dredging_dump |
-3011 |
4212053 |
-0.0007149 |
0.9994 |
|
| industry |
175.5 |
684714 |
0.0002563 |
0.9998 |
|
| shipping_mooring |
1301 |
2289608 |
0.0005684 |
0.9995 |
|
| shipping_traffic |
-306.1 |
1034133 |
-0.000296 |
0.9998 |
|
| sewers_rain |
2078 |
3222204 |
0.0006449 |
0.9995 |
|
| sewers_waste |
-4166 |
5992940 |
-0.0006951 |
0.9994 |
|
| wharves_city |
5085 |
7705365 |
0.0006599 |
0.9995 |
|
| wharves_industry |
1573 |
6353257 |
0.0002475 |
0.9998 |
|
| fisheries_trap |
33.55 |
70070 |
0.0004789 |
0.9996 |
|
| fisheries_trawl |
-31.83 |
157892 |
-0.0002016 |
0.9998 |
|
| fisheries_net |
51.54 |
6457820 |
7.982e-06 |
1 |
|
| fisheries_dredge |
-372.6 |
1064505 |
-0.00035 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
61.5 |
153 |
121 |
101 |
9.81 |
58.8 |
27.1 |
93.6 |
148 |
228 |
142 |
3.81 |
1.15 |
1 |
22.5 |

Periploma leanum
## SDM for: periploma_leanum
Abiotic parameters
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.1
## Pearson's pseudo-R2 is: 0.08
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-132.2 |
27635 |
-0.004783 |
0.9962 |
|
| om |
-1.575 |
1.573 |
-1.001 |
0.3167 |
|
| gravel |
-28.83 |
18298 |
-0.001576 |
0.9987 |
|
| silt |
1.055 |
1.528 |
0.6903 |
0.49 |
|
| clay |
-647.1 |
148289 |
-0.004364 |
0.9965 |
|
| arsenic |
0.4741 |
0.8081 |
0.5867 |
0.5574 |
|
| cadmium |
1.38 |
1.58 |
0.8734 |
0.3824 |
|
| copper |
2.51 |
2.493 |
1.007 |
0.3139 |
|
| iron |
-4.714 |
5.11 |
-0.9225 |
0.3563 |
|
| manganese |
0.7085 |
1.582 |
0.4478 |
0.6543 |
|
| mercury |
0.77 |
0.7267 |
1.06 |
0.2894 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.9 |
1 |
1.76 |
1 |
1.62 |
2.2 |
4.26 |
6.77 |
2.71 |
1.39 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-227.7 |
1501082 |
-0.0001517 |
0.9999 |
|
| aquaculture |
-655.6 |
8111491 |
-8.083e-05 |
0.9999 |
|
| city |
-665.8 |
7258664 |
-9.173e-05 |
0.9999 |
|
| dredging_collect |
164.9 |
11520200 |
1.431e-05 |
1 |
|
| dredging_dump |
426.6 |
5211430 |
8.186e-05 |
0.9999 |
|
| industry |
455.9 |
1040340 |
0.0004382 |
0.9997 |
|
| shipping_mooring |
-171.4 |
6466121 |
-2.651e-05 |
1 |
|
| shipping_traffic |
-37.4 |
2798229 |
-1.337e-05 |
1 |
|
| sewers_rain |
486.1 |
3734885 |
0.0001301 |
0.9999 |
|
| sewers_waste |
-644.6 |
3762103 |
-0.0001713 |
0.9999 |
|
| wharves_city |
742.8 |
9029972 |
8.225e-05 |
0.9999 |
|
| wharves_industry |
-1161 |
10311613 |
-0.0001126 |
0.9999 |
|
| fisheries_trap |
15.16 |
260244 |
5.824e-05 |
1 |
|
| fisheries_trawl |
-171.4 |
2187840 |
-7.834e-05 |
0.9999 |
|
| fisheries_net |
11.81 |
290916 |
4.059e-05 |
1 |
|
| fisheries_dredge |
-64.5 |
8989617 |
-7.175e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
86.8 |
63.7 |
117 |
58.7 |
15.1 |
78.3 |
32.9 |
40.1 |
43.2 |
80.9 |
113 |
1.49 |
15.9 |
1.15 |
28.8 |

Philine lima
## SDM for: philine_lima
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-157.4 |
448856 |
-0.0003506 |
0.9997 |
|
| om |
-9.446 |
244648 |
-3.861e-05 |
1 |
|
| gravel |
1.884 |
549794 |
3.427e-06 |
1 |
|
| silt |
25.73 |
120585 |
0.0002134 |
0.9998 |
|
| clay |
3.612 |
2041819 |
1.769e-06 |
1 |
|
| arsenic |
-99.38 |
527677 |
-0.0001883 |
0.9998 |
|
| cadmium |
46.64 |
389449 |
0.0001198 |
0.9999 |
|
| copper |
7.77 |
320737 |
2.423e-05 |
1 |
|
| iron |
5.156 |
458233 |
1.125e-05 |
1 |
|
| manganese |
-96.32 |
760707 |
-0.0001266 |
0.9999 |
|
| mercury |
-33.49 |
301617 |
-0.000111 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.49 |
2.61 |
2.07 |
1.42 |
1.71 |
5.8 |
3.83 |
4.91 |
6.15 |
2.97 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-132 |
261580 |
-0.0005048 |
0.9996 |
|
| aquaculture |
29.9 |
2012429 |
1.486e-05 |
1 |
|
| city |
-44.81 |
2524621 |
-1.775e-05 |
1 |
|
| dredging_collect |
-159.7 |
1088442 |
-0.0001467 |
0.9999 |
|
| dredging_dump |
-24.51 |
2547376 |
-9.621e-06 |
1 |
|
| industry |
-32.52 |
1014058 |
-3.207e-05 |
1 |
|
| shipping_mooring |
-1.868 |
1479903 |
-1.262e-06 |
1 |
|
| shipping_traffic |
-16.82 |
413635 |
-4.066e-05 |
1 |
|
| sewers_rain |
-9.975 |
1818813 |
-5.484e-06 |
1 |
|
| sewers_waste |
47.74 |
3083842 |
1.548e-05 |
1 |
|
| wharves_city |
64.8 |
3296940 |
1.965e-05 |
1 |
|
| wharves_industry |
194.3 |
1348004 |
0.0001441 |
0.9999 |
|
| fisheries_trap |
-166.4 |
673787 |
-0.0002469 |
0.9998 |
|
| fisheries_trawl |
-108.3 |
748721 |
-0.0001446 |
0.9999 |
|
| fisheries_net |
5.202 |
183416 |
2.836e-05 |
1 |
|
| fisheries_dredge |
51.23 |
177084 |
0.0002893 |
0.9998 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
30.6 |
29.4 |
14.3 |
34.2 |
9.02 |
22.4 |
6.04 |
31.7 |
53.2 |
36.3 |
18.3 |
2.25 |
2.44 |
1.2 |
2.14 |

Philomedes sp
## SDM for: philomedes_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.18
## Pearson's pseudo-R2 is: 0.17
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-330.6 |
87995 |
-0.003757 |
0.997 |
|
| om |
0.4032 |
1.74 |
0.2317 |
0.8168 |
|
| gravel |
0.2978 |
0.4143 |
0.7189 |
0.4722 |
|
| silt |
-0.6917 |
1.525 |
-0.4535 |
0.6502 |
|
| clay |
-1780 |
480174 |
-0.003706 |
0.997 |
|
| arsenic |
0.8166 |
1.255 |
0.6508 |
0.5152 |
|
| cadmium |
-0.4933 |
1.402 |
-0.3518 |
0.725 |
|
| copper |
-0.6786 |
1.526 |
-0.4448 |
0.6565 |
|
| iron |
-0.4185 |
0.7183 |
-0.5827 |
0.5601 |
|
| manganese |
0.1148 |
2.364 |
0.04858 |
0.9613 |
|
| mercury |
-1.455 |
2.229 |
-0.6528 |
0.5139 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.26 |
2.18 |
1 |
1.5 |
1.77 |
1.67 |
1.38 |
1.92 |
1.74 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-91.36 |
232639 |
-0.0003927 |
0.9997 |
|
| aquaculture |
-414.9 |
2683553 |
-0.0001546 |
0.9999 |
|
| city |
-194.1 |
3217121 |
-6.033e-05 |
1 |
|
| dredging_collect |
-178.4 |
877096 |
-0.0002034 |
0.9998 |
|
| dredging_dump |
-53.72 |
1830493 |
-2.935e-05 |
1 |
|
| industry |
196.9 |
620876 |
0.0003171 |
0.9997 |
|
| shipping_mooring |
-321 |
1479941 |
-0.0002169 |
0.9998 |
|
| shipping_traffic |
-17.55 |
607726 |
-2.888e-05 |
1 |
|
| sewers_rain |
68.12 |
3623940 |
1.88e-05 |
1 |
|
| sewers_waste |
-107 |
4876340 |
-2.194e-05 |
1 |
|
| wharves_city |
354.4 |
3932131 |
9.014e-05 |
0.9999 |
|
| wharves_industry |
75.82 |
2040465 |
3.716e-05 |
1 |
|
| fisheries_trap |
-63.41 |
409896 |
-0.0001547 |
0.9999 |
|
| fisheries_trawl |
5.201 |
116477 |
4.466e-05 |
1 |
|
| fisheries_net |
10.04 |
252488 |
3.976e-05 |
1 |
|
| fisheries_dredge |
30.14 |
179294 |
0.0001681 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
33.4 |
56.1 |
18.8 |
37 |
13.8 |
22.1 |
9.96 |
45.5 |
60.1 |
70.1 |
40.6 |
2.64 |
2.69 |
1.65 |
3.17 |

Pholoe longa
## SDM for: pholoe_longa
Abiotic parameters
## McFadden's pseudo-R2 is: -12.23
## Tjur's pseudo-R2 is: -0.01
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-3.301e+15 |
7978909 |
-413766906 |
0 |
* * * |
| om |
-1.493e+15 |
14257309 |
-104685824 |
0 |
* * * |
| gravel |
7.237e+14 |
8714549 |
83047230 |
0 |
* * * |
| silt |
1.384e+15 |
15274819 |
90612828 |
0 |
* * * |
| clay |
-7.527e+14 |
21858102 |
-34434528 |
0 |
* * * |
| arsenic |
-1.752e+15 |
11951982 |
-146562517 |
0 |
* * * |
| cadmium |
6.267e+14 |
10400421 |
60260861 |
0 |
* * * |
| copper |
1.939e+15 |
13687347 |
141670271 |
0 |
* * * |
| iron |
-4.869e+14 |
9620115 |
-50615564 |
0 |
* * * |
| manganese |
-5.417e+14 |
14288707 |
-37912203 |
0 |
* * * |
| mercury |
-6.123e+14 |
12068606 |
-50738739 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-108.2 |
230852 |
-0.0004686 |
0.9996 |
|
| aquaculture |
-372.7 |
7074900 |
-5.267e-05 |
1 |
|
| city |
-426.4 |
3308637 |
-0.0001289 |
0.9999 |
|
| dredging_collect |
-235.4 |
21696254 |
-1.085e-05 |
1 |
|
| dredging_dump |
-82.52 |
6321537 |
-1.305e-05 |
1 |
|
| industry |
213.3 |
1182221 |
0.0001805 |
0.9999 |
|
| shipping_mooring |
-280.9 |
11462006 |
-2.451e-05 |
1 |
|
| shipping_traffic |
-98.85 |
3244147 |
-3.047e-05 |
1 |
|
| sewers_rain |
103.2 |
6422833 |
1.607e-05 |
1 |
|
| sewers_waste |
-34.09 |
8749498 |
-3.896e-06 |
1 |
|
| wharves_city |
620.2 |
4779428 |
0.0001298 |
0.9999 |
|
| wharves_industry |
91.44 |
28604720 |
3.197e-06 |
1 |
|
| fisheries_trap |
-14.2 |
464038 |
-3.06e-05 |
1 |
|
| fisheries_trawl |
11.79 |
560768 |
2.102e-05 |
1 |
|
| fisheries_net |
5.979 |
272327 |
2.195e-05 |
1 |
|
| fisheries_dredge |
40.26 |
6081530 |
6.621e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
58.6 |
47.9 |
255 |
80.8 |
13 |
103 |
34.1 |
46.3 |
61.4 |
78.8 |
312 |
9.76 |
3.81 |
1.77 |
36.1 |

Pholoe sp
## SDM for: pholoe_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.17
## Tjur's pseudo-R2 is: 0.21
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-0.4394 |
0.3545 |
-1.239 |
0.2152 |
|
| om |
0.8286 |
0.4832 |
1.715 |
0.08637 |
|
| gravel |
-0.1573 |
0.2861 |
-0.55 |
0.5824 |
|
| silt |
0.01993 |
0.5099 |
0.03908 |
0.9688 |
|
| clay |
-1.015 |
1.431 |
-0.7091 |
0.4782 |
|
| arsenic |
-1.326 |
0.6602 |
-2.008 |
0.04465 |
* |
| cadmium |
-0.8395 |
0.4231 |
-1.984 |
0.04722 |
* |
| copper |
0.5358 |
0.5223 |
1.026 |
0.305 |
|
| iron |
-0.8158 |
0.4512 |
-1.808 |
0.0706 |
|
| manganese |
0.9999 |
0.5928 |
1.687 |
0.09163 |
|
| mercury |
-0.4265 |
0.4402 |
-0.9688 |
0.3326 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.92 |
1.38 |
2.08 |
1.03 |
1.87 |
1.51 |
2.03 |
1.6 |
2.32 |
1.64 |

Influence indices
## McFadden's pseudo-R2 is: 0.25
## Tjur's pseudo-R2 is: 0.3
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-56.59 |
6248 |
-0.009058 |
0.9928 |
|
| aquaculture |
-2.655 |
2.519 |
-1.054 |
0.2919 |
|
| city |
2.949 |
2.544 |
1.159 |
0.2465 |
|
| dredging_collect |
2.485 |
2.047 |
1.214 |
0.2248 |
|
| dredging_dump |
5.83 |
2.541 |
2.294 |
0.02177 |
* |
| industry |
0.6433 |
1.068 |
0.6023 |
0.547 |
|
| shipping_mooring |
0.5186 |
1.897 |
0.2734 |
0.7846 |
|
| shipping_traffic |
1.022 |
1.17 |
0.8732 |
0.3826 |
|
| sewers_rain |
5.001 |
2.62 |
1.909 |
0.05632 |
|
| sewers_waste |
-8.576 |
3.618 |
-2.371 |
0.01776 |
* |
| wharves_city |
-4.4 |
2.936 |
-1.499 |
0.1339 |
|
| wharves_industry |
-7.855 |
3.885 |
-2.022 |
0.04319 |
* |
| fisheries_trap |
0.03443 |
0.2453 |
0.1404 |
0.8884 |
|
| fisheries_trawl |
-1.74 |
1.004 |
-1.733 |
0.08307 |
|
| fisheries_net |
-585.2 |
64787 |
-0.009033 |
0.9928 |
|
| fisheries_dredge |
1.384 |
1.235 |
1.121 |
0.2624 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
10.5 |
10 |
7.59 |
9.54 |
4.18 |
7.06 |
4.59 |
9.29 |
13.8 |
11.8 |
14.4 |
1.12 |
1.79 |
1 |
2.22 |

Phoxocephalus holbolli
## SDM for: phoxocephalus_holbolli
Abiotic parameters
## McFadden's pseudo-R2 is: 0.18
## Tjur's pseudo-R2 is: 0.19
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.719 |
0.4479 |
-3.837 |
0.0001243 |
* * * |
| om |
-0.81 |
0.6594 |
-1.228 |
0.2193 |
|
| gravel |
-0.1437 |
0.3604 |
-0.3988 |
0.69 |
|
| silt |
-0.1046 |
0.5294 |
-0.1976 |
0.8434 |
|
| clay |
0.6938 |
0.7053 |
0.9837 |
0.3253 |
|
| arsenic |
-0.5911 |
1.013 |
-0.5835 |
0.5596 |
|
| cadmium |
0.6453 |
0.4406 |
1.465 |
0.143 |
|
| copper |
-0.05452 |
0.6256 |
-0.08714 |
0.9306 |
|
| iron |
-0.2543 |
0.6658 |
-0.3819 |
0.7025 |
|
| manganese |
-0.3255 |
0.8668 |
-0.3756 |
0.7072 |
|
| mercury |
-0.1344 |
0.6075 |
-0.2212 |
0.825 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.51 |
1.16 |
1.64 |
1.12 |
1.67 |
1.44 |
1.82 |
1.65 |
1.88 |
1.39 |

Influence indices
## McFadden's pseudo-R2 is: 0.5
## Tjur's pseudo-R2 is: 0.48
## Pearson's pseudo-R2 is: 0.47
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
66.08 |
7772 |
0.008502 |
0.9932 |
|
| aquaculture |
-0.8315 |
4.123 |
-0.2017 |
0.8402 |
|
| city |
10.56 |
4.67 |
2.262 |
0.02369 |
* |
| dredging_collect |
6.725 |
3.752 |
1.793 |
0.07305 |
|
| dredging_dump |
12.31 |
6.166 |
1.997 |
0.04581 |
* |
| industry |
-7.638 |
3.461 |
-2.207 |
0.02734 |
* |
| shipping_mooring |
1.01 |
3.695 |
0.2734 |
0.7846 |
|
| shipping_traffic |
1.118 |
2.386 |
0.4687 |
0.6393 |
|
| sewers_rain |
13.34 |
6.982 |
1.91 |
0.05608 |
|
| sewers_waste |
-22.85 |
11.26 |
-2.029 |
0.04245 |
* |
| wharves_city |
-17.13 |
6.755 |
-2.535 |
0.01124 |
* |
| wharves_industry |
-9.149 |
5.448 |
-1.679 |
0.09309 |
|
| fisheries_trap |
0.7861 |
0.3627 |
2.168 |
0.03019 |
* |
| fisheries_trawl |
-7.368 |
10.83 |
-0.6803 |
0.4963 |
|
| fisheries_net |
743.6 |
80587 |
0.009227 |
0.9926 |
|
| fisheries_dredge |
-2.166 |
1.243 |
-1.743 |
0.08135 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.1 |
14.4 |
9.72 |
15.9 |
9.82 |
8.51 |
6.87 |
19.7 |
29.2 |
19.7 |
13.9 |
1.49 |
2.45 |
1 |
2.1 |

Polynoidae spp
## SDM for: polynoidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.24
## Tjur's pseudo-R2 is: 0.26
## Pearson's pseudo-R2 is: 0.25
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.339 |
0.4137 |
-3.237 |
0.00121 |
* * |
| om |
2.01 |
0.6543 |
3.073 |
0.002121 |
* * |
| gravel |
-0.7474 |
0.4494 |
-1.663 |
0.09631 |
|
| silt |
-0.661 |
0.5851 |
-1.13 |
0.2586 |
|
| clay |
-0.07217 |
1.653 |
-0.04367 |
0.9652 |
|
| arsenic |
-0.1377 |
0.6559 |
-0.2099 |
0.8338 |
|
| cadmium |
-0.6335 |
0.5594 |
-1.133 |
0.2574 |
|
| copper |
-0.544 |
0.684 |
-0.7953 |
0.4264 |
|
| iron |
-0.8514 |
0.7809 |
-1.09 |
0.2756 |
|
| manganese |
-0.163 |
0.726 |
-0.2245 |
0.8224 |
|
| mercury |
0.7763 |
0.5309 |
1.462 |
0.1437 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.29 |
1.2 |
2.05 |
1.05 |
1.8 |
1.76 |
2.38 |
2.18 |
2.43 |
1.86 |

Influence indices
## McFadden's pseudo-R2 is: 0.2
## Tjur's pseudo-R2 is: 0.2
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-73.68 |
8065 |
-0.009136 |
0.9927 |
|
| aquaculture |
-2.528 |
2.749 |
-0.9195 |
0.3578 |
|
| city |
-2.859 |
2.665 |
-1.073 |
0.2833 |
|
| dredging_collect |
3.45 |
2.226 |
1.55 |
0.1211 |
|
| dredging_dump |
1.889 |
2.39 |
0.7903 |
0.4293 |
|
| industry |
1.072 |
1.34 |
0.7998 |
0.4238 |
|
| shipping_mooring |
1.271 |
2.212 |
0.5748 |
0.5655 |
|
| shipping_traffic |
0.1962 |
1.092 |
0.1796 |
0.8574 |
|
| sewers_rain |
5.235 |
3.24 |
1.616 |
0.1061 |
|
| sewers_waste |
-6.783 |
4.038 |
-1.68 |
0.09304 |
|
| wharves_city |
1.556 |
3.141 |
0.4954 |
0.6203 |
|
| wharves_industry |
-7.486 |
4.055 |
-1.846 |
0.06484 |
|
| fisheries_trap |
-2.591 |
1.874 |
-1.383 |
0.1668 |
|
| fisheries_trawl |
-1.057 |
0.9408 |
-1.123 |
0.2614 |
|
| fisheries_net |
-747.9 |
83622 |
-0.008943 |
0.9929 |
|
| fisheries_dredge |
0.2029 |
0.8197 |
0.2476 |
0.8045 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.1 |
8.87 |
7.51 |
8.28 |
4.93 |
8.5 |
3.89 |
11.3 |
14.9 |
10.3 |
13.6 |
1.29 |
2.06 |
1 |
1.66 |

Pontogeneia inermis
## SDM for: pontogeneia_inermis
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-113.7 |
251118 |
-0.0004526 |
0.9996 |
|
| om |
-50.31 |
441711 |
-0.0001139 |
0.9999 |
|
| gravel |
32.36 |
153775 |
0.0002104 |
0.9998 |
|
| silt |
68.19 |
420624 |
0.0001621 |
0.9999 |
|
| clay |
-9.711 |
506273 |
-1.918e-05 |
1 |
|
| arsenic |
-45.88 |
1074917 |
-4.269e-05 |
1 |
|
| cadmium |
-53.4 |
598912 |
-8.916e-05 |
0.9999 |
|
| copper |
99.55 |
669205 |
0.0001488 |
0.9999 |
|
| iron |
8.833 |
665738 |
1.327e-05 |
1 |
|
| manganese |
-19 |
312188 |
-6.085e-05 |
1 |
|
| mercury |
-6.325 |
479027 |
-1.32e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
5.05 |
1.52 |
5.08 |
1.51 |
7.72 |
7.08 |
8.96 |
6.4 |
4.43 |
8.2 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-132.4 |
255619 |
-0.000518 |
0.9996 |
|
| aquaculture |
-264.7 |
2849030 |
-9.291e-05 |
0.9999 |
|
| city |
-180.8 |
1027209 |
-0.000176 |
0.9999 |
|
| dredging_collect |
-77.17 |
1149361 |
-6.715e-05 |
0.9999 |
|
| dredging_dump |
156.6 |
2889772 |
5.42e-05 |
1 |
|
| industry |
71.68 |
660587 |
0.0001085 |
0.9999 |
|
| shipping_mooring |
-275.5 |
1058157 |
-0.0002604 |
0.9998 |
|
| shipping_traffic |
-46.12 |
442675 |
-0.0001042 |
0.9999 |
|
| sewers_rain |
46.86 |
4431384 |
1.057e-05 |
1 |
|
| sewers_waste |
-30.89 |
6090006 |
-5.073e-06 |
1 |
|
| wharves_city |
262.6 |
1727998 |
0.000152 |
0.9999 |
|
| wharves_industry |
-70.36 |
2673518 |
-2.632e-05 |
1 |
|
| fisheries_trap |
-189.2 |
430303 |
-0.0004398 |
0.9996 |
|
| fisheries_trawl |
11.99 |
407278 |
2.943e-05 |
1 |
|
| fisheries_net |
14.46 |
261862 |
5.523e-05 |
1 |
|
| fisheries_dredge |
0.9666 |
534177 |
1.809e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
24 |
15.4 |
15.2 |
40.7 |
9.2 |
11.4 |
5.49 |
44.4 |
59.3 |
28.7 |
35 |
1.66 |
3.52 |
1.71 |
2.89 |

Pontoporeia femorata
## SDM for: pontoporeia_femorata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.36
## Tjur's pseudo-R2 is: 0.37
## Pearson's pseudo-R2 is: 0.36
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-338 |
56324 |
-0.006001 |
0.9952 |
|
| om |
0.279 |
0.6352 |
0.4393 |
0.6604 |
|
| gravel |
0.1841 |
0.5257 |
0.3501 |
0.7262 |
|
| silt |
0.893 |
0.855 |
1.044 |
0.2963 |
|
| clay |
-1835 |
307350 |
-0.00597 |
0.9952 |
|
| arsenic |
-1.69 |
0.8365 |
-2.02 |
0.04338 |
* |
| cadmium |
0.4184 |
0.4318 |
0.9691 |
0.3325 |
|
| copper |
0.6055 |
0.5924 |
1.022 |
0.3067 |
|
| iron |
0.04479 |
0.6532 |
0.06857 |
0.9453 |
|
| manganese |
1.063 |
0.6523 |
1.629 |
0.1032 |
|
| mercury |
0.02013 |
0.5123 |
0.0393 |
0.9687 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.86 |
1.41 |
2.14 |
1 |
1.82 |
1.24 |
1.69 |
1.5 |
1.64 |
1.43 |

Influence indices
## McFadden's pseudo-R2 is: 0.41
## Tjur's pseudo-R2 is: 0.42
## Pearson's pseudo-R2 is: 0.44
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-255.5 |
81485 |
-0.003136 |
0.9975 |
|
| aquaculture |
0.5583 |
4.602 |
0.1213 |
0.9034 |
|
| city |
-1.779 |
2.748 |
-0.6475 |
0.5173 |
|
| dredging_collect |
-9.96 |
6.483 |
-1.536 |
0.1245 |
|
| dredging_dump |
2.715 |
2.717 |
0.999 |
0.3178 |
|
| industry |
-5.829 |
3.439 |
-1.695 |
0.09006 |
|
| shipping_mooring |
1.908 |
2.367 |
0.8059 |
0.4203 |
|
| shipping_traffic |
2.658 |
2.88 |
0.923 |
0.356 |
|
| sewers_rain |
0.5592 |
3.663 |
0.1527 |
0.8787 |
|
| sewers_waste |
-0.7551 |
5.062 |
-0.1492 |
0.8814 |
|
| wharves_city |
0.4469 |
3.481 |
0.1284 |
0.8979 |
|
| wharves_industry |
9.532 |
7.268 |
1.312 |
0.1897 |
|
| fisheries_trap |
0.3215 |
0.2973 |
1.081 |
0.2795 |
|
| fisheries_trawl |
-646.4 |
242747 |
-0.002663 |
0.9979 |
|
| fisheries_net |
-755.9 |
483832 |
-0.001562 |
0.9988 |
|
| fisheries_dredge |
-22.76 |
53107 |
-0.0004286 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
8.18 |
8.85 |
17.1 |
7.51 |
10.4 |
3.05 |
8.43 |
6.2 |
7.39 |
12 |
19.9 |
1.35 |
1 |
1 |
1 |

Praxillella praetermissa
## SDM for: praxillella_praetermissa
Abiotic parameters
## McFadden's pseudo-R2 is: 0.22
## Tjur's pseudo-R2 is: 0.18
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-10.4 |
1162 |
-0.008951 |
0.9929 |
|
| om |
-0.09507 |
0.6811 |
-0.1396 |
0.889 |
|
| gravel |
-27.89 |
4150 |
-0.006722 |
0.9946 |
|
| silt |
0.3509 |
0.9224 |
0.3804 |
0.7036 |
|
| clay |
-1.636 |
5.059 |
-0.3234 |
0.7464 |
|
| arsenic |
0.2253 |
0.4521 |
0.4984 |
0.6182 |
|
| cadmium |
0.07706 |
0.6023 |
0.128 |
0.8982 |
|
| copper |
0.3128 |
0.9471 |
0.3303 |
0.7412 |
|
| iron |
-0.9361 |
1.692 |
-0.5532 |
0.5801 |
|
| manganese |
0.8187 |
0.7688 |
1.065 |
0.2869 |
|
| mercury |
0.2264 |
0.5489 |
0.4124 |
0.6801 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.83 |
1 |
1.98 |
1.01 |
1.55 |
1.35 |
2.48 |
3.24 |
2.04 |
1.39 |

Influence indices
## McFadden's pseudo-R2 is: 0.53
## Tjur's pseudo-R2 is: 0.45
## Pearson's pseudo-R2 is: 0.43
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-69.75 |
10367 |
-0.006728 |
0.9946 |
|
| aquaculture |
11.62 |
7.473 |
1.555 |
0.12 |
|
| city |
-24.97 |
25.45 |
-0.9812 |
0.3265 |
|
| dredging_collect |
-10.97 |
10.81 |
-1.015 |
0.3103 |
|
| dredging_dump |
-5.416 |
6.608 |
-0.8197 |
0.4124 |
|
| industry |
-2.359 |
3.242 |
-0.7278 |
0.4667 |
|
| shipping_mooring |
17.37 |
11.77 |
1.476 |
0.14 |
|
| shipping_traffic |
4.75 |
4.314 |
1.101 |
0.2708 |
|
| sewers_rain |
-3.62 |
5.494 |
-0.6589 |
0.51 |
|
| sewers_waste |
-0.9263 |
10.55 |
-0.08779 |
0.93 |
|
| wharves_city |
17.38 |
20.17 |
0.8616 |
0.3889 |
|
| wharves_industry |
15.85 |
12.15 |
1.305 |
0.192 |
|
| fisheries_trap |
0.8655 |
2.4 |
0.3606 |
0.7184 |
|
| fisheries_trawl |
-58.94 |
62.08 |
-0.9494 |
0.3424 |
|
| fisheries_net |
-482.6 |
107495 |
-0.00449 |
0.9964 |
|
| fisheries_dredge |
5.239 |
3.934 |
1.332 |
0.183 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.9 |
41.9 |
15.8 |
9.85 |
3.8 |
17 |
7.06 |
9.09 |
18.4 |
36.3 |
18.3 |
1.59 |
1.55 |
1 |
3.47 |

Propebela turricula
## SDM for: propebela_turricula
Abiotic parameters
## McFadden's pseudo-R2 is: 0.16
## Tjur's pseudo-R2 is: 0.05
## Pearson's pseudo-R2 is: 0.04
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-127.3 |
23088 |
-0.005513 |
0.9956 |
|
| om |
0.4587 |
1.11 |
0.4133 |
0.6794 |
|
| gravel |
-32.1 |
18934 |
-0.001695 |
0.9986 |
|
| silt |
-0.2984 |
1.114 |
-0.2679 |
0.7888 |
|
| clay |
-629.1 |
122636 |
-0.00513 |
0.9959 |
|
| arsenic |
0.03976 |
1.277 |
0.03113 |
0.9752 |
|
| cadmium |
-0.8077 |
0.9998 |
-0.8078 |
0.4192 |
|
| copper |
0.1158 |
1.323 |
0.08747 |
0.9303 |
|
| iron |
-1.621 |
1.957 |
-0.8286 |
0.4073 |
|
| manganese |
1.113 |
1.688 |
0.6596 |
0.5095 |
|
| mercury |
-0.1672 |
1.129 |
-0.148 |
0.8823 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.99 |
1 |
2.01 |
1 |
1.64 |
1.61 |
2.22 |
2.74 |
2.96 |
1.98 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-378.6 |
994430 |
-0.0003807 |
0.9997 |
|
| aquaculture |
959.8 |
2613836 |
0.0003672 |
0.9997 |
|
| city |
-890.5 |
10801018 |
-8.245e-05 |
0.9999 |
|
| dredging_collect |
524.7 |
1503707 |
0.0003489 |
0.9997 |
|
| dredging_dump |
809.3 |
4047658 |
2e-04 |
0.9998 |
|
| industry |
-311.4 |
2132905 |
-0.000146 |
0.9999 |
|
| shipping_mooring |
375 |
3907328 |
9.597e-05 |
0.9999 |
|
| shipping_traffic |
-153.9 |
1238146 |
-0.0001243 |
0.9999 |
|
| sewers_rain |
232.3 |
2590747 |
8.966e-05 |
0.9999 |
|
| sewers_waste |
653.7 |
6119282 |
0.0001068 |
0.9999 |
|
| wharves_city |
408.6 |
9943058 |
4.11e-05 |
1 |
|
| wharves_industry |
-1388 |
5004811 |
-0.0002774 |
0.9998 |
|
| fisheries_trap |
3.015 |
142198 |
2.12e-05 |
1 |
|
| fisheries_trawl |
-3.921 |
198222 |
-1.978e-05 |
1 |
|
| fisheries_net |
-15.48 |
6457684 |
-2.396e-06 |
1 |
|
| fisheries_dredge |
31.93 |
263961 |
0.0001209 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
36.4 |
86.5 |
23.4 |
53.4 |
28 |
48.4 |
19.8 |
49.2 |
105 |
83.5 |
74.5 |
1.65 |
3.79 |
1 |
3.4 |

Protomedeia fasciata
## SDM for: protomedeia_fasciata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.31
## Tjur's pseudo-R2 is: 0.22
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-286.4 |
49594 |
-0.005774 |
0.9954 |
|
| om |
-0.9114 |
0.8312 |
-1.096 |
0.2729 |
|
| gravel |
1.222 |
0.6627 |
1.844 |
0.06519 |
|
| silt |
2.751 |
1.511 |
1.821 |
0.06863 |
|
| clay |
-1540 |
270625 |
-0.005689 |
0.9955 |
|
| arsenic |
-4.199 |
2.57 |
-1.634 |
0.1023 |
|
| cadmium |
0.1073 |
0.745 |
0.1441 |
0.8854 |
|
| copper |
0.8999 |
0.9298 |
0.9678 |
0.3332 |
|
| iron |
-0.07938 |
1.108 |
-0.07165 |
0.9429 |
|
| manganese |
2.454 |
1.641 |
1.495 |
0.1348 |
|
| mercury |
-1.888 |
1.306 |
-1.445 |
0.1484 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.99 |
2.61 |
3.27 |
1 |
2.52 |
1.63 |
1.84 |
1.89 |
3.98 |
3.22 |

Influence indices
## McFadden's pseudo-R2 is: 0.62
## Tjur's pseudo-R2 is: 0.56
## Pearson's pseudo-R2 is: 0.58
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-30.43 |
42.77 |
-0.7116 |
0.4767 |
|
| aquaculture |
-33.98 |
30.52 |
-1.113 |
0.2656 |
|
| city |
-17.06 |
12.63 |
-1.351 |
0.1768 |
|
| dredging_collect |
-29.37 |
23.21 |
-1.266 |
0.2056 |
|
| dredging_dump |
107.7 |
84.2 |
1.279 |
0.201 |
|
| industry |
-27.26 |
21.36 |
-1.276 |
0.2018 |
|
| shipping_mooring |
-83.28 |
64.75 |
-1.286 |
0.1984 |
|
| shipping_traffic |
-17.08 |
13.59 |
-1.257 |
0.2087 |
|
| sewers_rain |
-25.16 |
23.1 |
-1.089 |
0.2761 |
|
| sewers_waste |
26.29 |
30.99 |
0.8485 |
0.3962 |
|
| wharves_city |
10.62 |
12.42 |
0.8548 |
0.3927 |
|
| wharves_industry |
-3.717 |
14.13 |
-0.2631 |
0.7925 |
|
| fisheries_trap |
-1.81 |
1.412 |
-1.282 |
0.1999 |
|
| fisheries_trawl |
3.466 |
5.639 |
0.6146 |
0.5388 |
|
| fisheries_net |
5.945 |
379.9 |
0.01565 |
0.9875 |
|
| fisheries_dredge |
-6.132 |
5.275 |
-1.162 |
0.2451 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
57.6 |
25.7 |
42 |
144 |
25.7 |
115 |
16.5 |
33.3 |
52.3 |
27.3 |
23.8 |
3.99 |
9.89 |
1 |
4.96 |

Protomedeia grandimana
## SDM for: protomedeia_grandimana
Abiotic parameters
## McFadden's pseudo-R2 is: 0.33
## Tjur's pseudo-R2 is: 0.37
## Pearson's pseudo-R2 is: 0.36
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.191 |
2.245 |
-0.5303 |
0.5959 |
|
| om |
2.011 |
0.6965 |
2.887 |
0.003884 |
* * |
| gravel |
-0.04722 |
0.3597 |
-0.1313 |
0.8956 |
|
| silt |
-0.8222 |
0.6085 |
-1.351 |
0.1766 |
|
| clay |
-6.299 |
12.26 |
-0.5137 |
0.6074 |
|
| arsenic |
-1.32 |
0.8339 |
-1.583 |
0.1133 |
|
| cadmium |
-1.239 |
0.619 |
-2.002 |
0.04532 |
* |
| copper |
0.5195 |
0.6455 |
0.8048 |
0.4209 |
|
| iron |
-1.046 |
0.7607 |
-1.375 |
0.1691 |
|
| manganese |
0.7802 |
0.803 |
0.9715 |
0.3313 |
|
| mercury |
1.809 |
0.7619 |
2.375 |
0.01756 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.22 |
1.18 |
2.21 |
1.04 |
2.31 |
1.99 |
2.34 |
2.09 |
2.37 |
1.97 |

Influence indices
## McFadden's pseudo-R2 is: 0.22
## Tjur's pseudo-R2 is: 0.27
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-63.73 |
6192 |
-0.01029 |
0.9918 |
|
| aquaculture |
-1.913 |
2.285 |
-0.8374 |
0.4024 |
|
| city |
-3.533 |
2.486 |
-1.421 |
0.1554 |
|
| dredging_collect |
1.517 |
1.78 |
0.8524 |
0.394 |
|
| dredging_dump |
5.225 |
2.127 |
2.457 |
0.01402 |
* |
| industry |
0.7829 |
1.061 |
0.7376 |
0.4608 |
|
| shipping_mooring |
1.015 |
1.879 |
0.5399 |
0.5893 |
|
| shipping_traffic |
-0.885 |
0.9425 |
-0.939 |
0.3477 |
|
| sewers_rain |
4.904 |
2.838 |
1.728 |
0.08403 |
|
| sewers_waste |
-5.241 |
3.599 |
-1.456 |
0.1453 |
|
| wharves_city |
2.582 |
2.771 |
0.9319 |
0.3514 |
|
| wharves_industry |
-7.184 |
3.205 |
-2.241 |
0.02501 |
* |
| fisheries_trap |
-0.3069 |
0.2535 |
-1.211 |
0.226 |
|
| fisheries_trawl |
-0.2174 |
0.4308 |
-0.5045 |
0.6139 |
|
| fisheries_net |
-660 |
64201 |
-0.01028 |
0.9918 |
|
| fisheries_dredge |
0.4733 |
0.6665 |
0.7101 |
0.4777 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.65 |
9.7 |
6.66 |
7.94 |
4.13 |
7.34 |
3.74 |
10.9 |
14.5 |
10.6 |
12.1 |
1.16 |
1.44 |
1 |
1.7 |

Puncturella noachina
## SDM for: puncturella_noachina
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-92.72 |
150483 |
-0.0006162 |
0.9995 |
|
| om |
-51.01 |
168846 |
-0.0003021 |
0.9998 |
|
| gravel |
26.9 |
70521 |
0.0003815 |
0.9997 |
|
| silt |
22.7 |
175221 |
0.0001296 |
0.9999 |
|
| clay |
2.983 |
256019 |
1.165e-05 |
1 |
|
| arsenic |
-38.2 |
199649 |
-0.0001913 |
0.9998 |
|
| cadmium |
29.85 |
97550 |
0.000306 |
0.9998 |
|
| copper |
30.54 |
185178 |
0.0001649 |
0.9999 |
|
| iron |
7.023 |
449909 |
1.561e-05 |
1 |
|
| manganese |
-28.08 |
250267 |
-0.0001122 |
0.9999 |
|
| mercury |
17.88 |
178947 |
9.994e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.01 |
3.95 |
3.8 |
1.1 |
2.74 |
2.8 |
4.52 |
5.78 |
4.55 |
3.87 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-67.54 |
140643 |
-0.0004802 |
0.9996 |
|
| aquaculture |
71.34 |
4842619 |
1.473e-05 |
1 |
|
| city |
145.3 |
10402474 |
1.397e-05 |
1 |
|
| dredging_collect |
77.29 |
774144 |
9.983e-05 |
0.9999 |
|
| dredging_dump |
-69.13 |
2155231 |
-3.207e-05 |
1 |
|
| industry |
46.71 |
2178614 |
2.144e-05 |
1 |
|
| shipping_mooring |
71.66 |
2724611 |
2.63e-05 |
1 |
|
| shipping_traffic |
44.96 |
2905192 |
1.548e-05 |
1 |
|
| sewers_rain |
-7.165 |
4554958 |
-1.573e-06 |
1 |
|
| sewers_waste |
13.34 |
2622642 |
5.087e-06 |
1 |
|
| wharves_city |
-175.1 |
10700829 |
-1.637e-05 |
1 |
|
| wharves_industry |
-93.09 |
1857286 |
-5.012e-05 |
1 |
|
| fisheries_trap |
6.767 |
578628 |
1.17e-05 |
1 |
|
| fisheries_trawl |
-4.491 |
150280 |
-2.989e-05 |
1 |
|
| fisheries_net |
2.735 |
404568 |
6.76e-06 |
1 |
|
| fisheries_dredge |
-8.065 |
912207 |
-8.842e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
78.2 |
177 |
16.8 |
40.9 |
43.9 |
52.4 |
45.4 |
67.8 |
41.7 |
170 |
36.4 |
8.18 |
4.68 |
4.35 |
5.38 |

Quasimelita quadrispinosa
## SDM for: quasimelita_quadrispinosa
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-164.2 |
390466 |
-0.0004205 |
0.9997 |
|
| om |
123.5 |
326201 |
0.0003785 |
0.9997 |
|
| gravel |
-37.77 |
359356 |
-0.0001051 |
0.9999 |
|
| silt |
-77.04 |
770127 |
-1e-04 |
0.9999 |
|
| clay |
98.65 |
686832 |
0.0001436 |
0.9999 |
|
| arsenic |
-142.2 |
711751 |
-0.0001998 |
0.9998 |
|
| cadmium |
-27.17 |
605434 |
-4.488e-05 |
1 |
|
| copper |
97.63 |
388985 |
0.000251 |
0.9998 |
|
| iron |
-116.7 |
737376 |
-0.0001583 |
0.9999 |
|
| manganese |
37.35 |
552277 |
6.762e-05 |
0.9999 |
|
| mercury |
30.18 |
251967 |
0.0001198 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.82 |
2.89 |
9.81 |
1.1 |
5.57 |
9.06 |
5.56 |
8.88 |
6.16 |
3.33 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-90.81 |
346796 |
-0.0002618 |
0.9998 |
|
| aquaculture |
337.7 |
14867673 |
2.271e-05 |
1 |
|
| city |
74.87 |
20706774 |
3.616e-06 |
1 |
|
| dredging_collect |
226.6 |
6417189 |
3.531e-05 |
1 |
|
| dredging_dump |
56.87 |
5003265 |
1.137e-05 |
1 |
|
| industry |
-175.2 |
1904624 |
-9.2e-05 |
0.9999 |
|
| shipping_mooring |
293.7 |
8995786 |
3.265e-05 |
1 |
|
| shipping_traffic |
66.97 |
5979316 |
1.12e-05 |
1 |
|
| sewers_rain |
-15.88 |
2149956 |
-7.387e-06 |
1 |
|
| sewers_waste |
25.65 |
7702137 |
3.331e-06 |
1 |
|
| wharves_city |
-192.5 |
20119042 |
-9.568e-06 |
1 |
|
| wharves_industry |
-235.9 |
2676196 |
-8.815e-05 |
0.9999 |
|
| fisheries_trap |
-7.447 |
345469 |
-2.156e-05 |
1 |
|
| fisheries_trawl |
-15.2 |
1249359 |
-1.216e-05 |
1 |
|
| fisheries_net |
-3.215 |
223978 |
-1.435e-05 |
1 |
|
| fisheries_dredge |
1.873 |
1597431 |
1.173e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
125 |
305 |
57.9 |
55.2 |
10.8 |
85.1 |
55.5 |
18.4 |
63.7 |
330 |
24.6 |
2.21 |
12.2 |
1.46 |
9.59 |

Retusa obtusa
## SDM for: retusa_obtusa
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-156.8 |
256927 |
-0.0006101 |
0.9995 |
|
| om |
-39.64 |
337208 |
-0.0001176 |
0.9999 |
|
| gravel |
22.06 |
109124 |
0.0002021 |
0.9998 |
|
| silt |
-1.387 |
248244 |
-5.588e-06 |
1 |
|
| clay |
27.6 |
382853 |
7.209e-05 |
0.9999 |
|
| arsenic |
-161.7 |
378101 |
-0.0004276 |
0.9997 |
|
| cadmium |
42.35 |
104673 |
0.0004046 |
0.9997 |
|
| copper |
79.79 |
178804 |
0.0004462 |
0.9996 |
|
| iron |
30.12 |
88462 |
0.0003405 |
0.9997 |
|
| manganese |
4.628 |
114437 |
4.044e-05 |
1 |
|
| mercury |
-16.22 |
189061 |
-8.579e-05 |
0.9999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
4.08 |
1.31 |
2.78 |
1.17 |
2.72 |
2.1 |
2.86 |
2.14 |
1.53 |
2.52 |

Influence indices
## McFadden's pseudo-R2 is: -5.68
## Tjur's pseudo-R2 is: 0.49
## Pearson's pseudo-R2 is: 0.31
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-2.698e+15 |
7496963 |
-359928090 |
0 |
* * * |
| aquaculture |
1.324e+15 |
66771996 |
19833430 |
0 |
* * * |
| city |
1.184e+15 |
60515998 |
19565080 |
0 |
* * * |
| dredging_collect |
2.363e+15 |
47886179 |
49336796 |
0 |
* * * |
| dredging_dump |
6.956e+15 |
55589796 |
125128640 |
0 |
* * * |
| industry |
-2.714e+15 |
30193191 |
-89885547 |
0 |
* * * |
| shipping_mooring |
-3.942e+14 |
51224704 |
-7696358 |
0 |
* * * |
| shipping_traffic |
-1.019e+15 |
22815885 |
-44683654 |
0 |
* * * |
| sewers_rain |
2.056e+15 |
67164046 |
30618588 |
0 |
* * * |
| sewers_waste |
-2.785e+15 |
90359677 |
-30820178 |
0 |
* * * |
| wharves_city |
-2.962e+15 |
72465526 |
-40880797 |
0 |
* * * |
| wharves_industry |
-4.323e+15 |
79067645 |
-54675006 |
0 |
* * * |
| fisheries_trap |
9.826e+13 |
7277539 |
13502424 |
0 |
* * * |
| fisheries_trawl |
3.331e+14 |
8821984 |
37763066 |
0 |
* * * |
| fisheries_net |
-1.628e+14 |
7219163 |
-22544924 |
0 |
* * * |
| fisheries_dredge |
-6.887e+14 |
19454028 |
-35400346 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Sabellidae spp
## SDM for: sabellidae_spp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.32
## Tjur's pseudo-R2 is: 0.29
## Pearson's pseudo-R2 is: 0.28
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-10.62 |
1174 |
-0.009047 |
0.9928 |
|
| om |
0.201 |
0.7698 |
0.261 |
0.7941 |
|
| gravel |
-29.67 |
4191 |
-0.007078 |
0.9944 |
|
| silt |
-0.7105 |
0.9274 |
-0.7661 |
0.4436 |
|
| clay |
1.382 |
0.8095 |
1.707 |
0.08782 |
|
| arsenic |
-1.324 |
1.148 |
-1.153 |
0.249 |
|
| cadmium |
1.347 |
0.6348 |
2.122 |
0.0338 |
* |
| copper |
-0.211 |
0.9202 |
-0.2293 |
0.8186 |
|
| iron |
-0.3527 |
1.546 |
-0.2282 |
0.8195 |
|
| manganese |
0.9739 |
0.8142 |
1.196 |
0.2317 |
|
| mercury |
0.5238 |
0.5284 |
0.9914 |
0.3215 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.01 |
1 |
2.15 |
1.25 |
1.8 |
1.3 |
2.27 |
2.69 |
1.95 |
1.38 |

Influence indices
## McFadden's pseudo-R2 is: 0.3
## Tjur's pseudo-R2 is: 0.25
## Pearson's pseudo-R2 is: 0.25
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-3.965 |
1.442 |
-2.75 |
0.005957 |
* * |
| aquaculture |
-1.386 |
3.387 |
-0.4091 |
0.6825 |
|
| city |
-0.01064 |
3.683 |
-0.002889 |
0.9977 |
|
| dredging_collect |
4.399 |
4.341 |
1.013 |
0.311 |
|
| dredging_dump |
3.071 |
3.483 |
0.8817 |
0.3779 |
|
| industry |
3.852 |
2.634 |
1.462 |
0.1436 |
|
| shipping_mooring |
3.234 |
2.87 |
1.127 |
0.2599 |
|
| shipping_traffic |
-2.907 |
3.062 |
-0.9495 |
0.3424 |
|
| sewers_rain |
8.219 |
5.193 |
1.583 |
0.1135 |
|
| sewers_waste |
-8.36 |
5.926 |
-1.411 |
0.1583 |
|
| wharves_city |
-3.732 |
3.934 |
-0.9485 |
0.3429 |
|
| wharves_industry |
-9.842 |
7.119 |
-1.382 |
0.1668 |
|
| fisheries_trap |
1.351 |
0.6097 |
2.217 |
0.02664 |
* |
| fisheries_trawl |
-1.344 |
4.293 |
-0.313 |
0.7543 |
|
| fisheries_net |
-0.5748 |
2.692 |
-0.2135 |
0.8309 |
|
| fisheries_dredge |
-2.504 |
2.829 |
-0.8851 |
0.3761 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
8.07 |
7.99 |
10.2 |
8.57 |
7.5 |
5.89 |
8.29 |
12.2 |
12.9 |
8.8 |
17.3 |
2.74 |
1.21 |
1 |
2.14 |

Scoletoma fragilis
## SDM for: scoletoma_fragilis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.59
## Tjur's pseudo-R2 is: 0.44
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-22.65 |
2240 |
-0.01011 |
0.9919 |
|
| om |
-0.4338 |
2.097 |
-0.2069 |
0.8361 |
|
| gravel |
-12.98 |
7996 |
-0.001623 |
0.9987 |
|
| silt |
10.33 |
7.262 |
1.423 |
0.1549 |
|
| clay |
2.615 |
2.168 |
1.206 |
0.2277 |
|
| arsenic |
-10.18 |
6.309 |
-1.613 |
0.1068 |
|
| cadmium |
1.243 |
2.017 |
0.616 |
0.5379 |
|
| copper |
14.27 |
8.768 |
1.627 |
0.1036 |
|
| iron |
-13.63 |
8.743 |
-1.559 |
0.119 |
|
| manganese |
11.01 |
6.346 |
1.734 |
0.08288 |
|
| mercury |
-9.345 |
5.604 |
-1.668 |
0.09536 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
3.57 |
1 |
7.89 |
1.54 |
7.5 |
1.89 |
11.5 |
7.98 |
9.08 |
7.2 |

Influence indices
## McFadden's pseudo-R2 is: 0.58
## Tjur's pseudo-R2 is: 0.47
## Pearson's pseudo-R2 is: 0.48
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-55.49 |
486.2 |
-0.1141 |
0.9091 |
|
| aquaculture |
-55.82 |
42.77 |
-1.305 |
0.1919 |
|
| city |
-148.3 |
93.87 |
-1.579 |
0.1142 |
|
| dredging_collect |
114.9 |
65.52 |
1.754 |
0.07942 |
|
| dredging_dump |
54.64 |
38.63 |
1.414 |
0.1573 |
|
| industry |
5.48 |
11.27 |
0.4863 |
0.6267 |
|
| shipping_mooring |
32.22 |
27.61 |
1.167 |
0.2433 |
|
| shipping_traffic |
-32.15 |
21.51 |
-1.495 |
0.1349 |
|
| sewers_rain |
88.02 |
56.16 |
1.567 |
0.1171 |
|
| sewers_waste |
-87.79 |
63.52 |
-1.382 |
0.1669 |
|
| wharves_city |
116.1 |
75.24 |
1.543 |
0.1229 |
|
| wharves_industry |
-151.3 |
88.18 |
-1.715 |
0.08625 |
|
| fisheries_trap |
-18.28 |
12.09 |
-1.512 |
0.1306 |
|
| fisheries_trawl |
12.49 |
1268 |
0.00985 |
0.9921 |
|
| fisheries_net |
0.4484 |
3.871 |
0.1158 |
0.9078 |
|
| fisheries_dredge |
2.826 |
1054 |
0.002682 |
0.9979 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
35 |
115 |
55.4 |
35 |
11.5 |
14.4 |
19.7 |
59.4 |
43.2 |
107 |
75.3 |
3.58 |
1 |
1.02 |
1 |

Scoletoma sp
## SDM for: scoletoma_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.66
## Tjur's pseudo-R2 is: 0.5
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-42.03 |
11416 |
-0.003682 |
0.9971 |
|
| om |
-16.62 |
19.01 |
-0.8743 |
0.382 |
|
| gravel |
-24.58 |
14777 |
-0.001663 |
0.9987 |
|
| silt |
5.955 |
7.944 |
0.7497 |
0.4535 |
|
| clay |
-59.72 |
59340 |
-0.001006 |
0.9992 |
|
| arsenic |
6.132 |
11.91 |
0.5147 |
0.6067 |
|
| cadmium |
-4.616 |
5.231 |
-0.8825 |
0.3775 |
|
| copper |
9.748 |
13 |
0.75 |
0.4532 |
|
| iron |
2.445 |
3.681 |
0.6642 |
0.5065 |
|
| manganese |
-14.07 |
25.76 |
-0.5463 |
0.5848 |
|
| mercury |
-10.48 |
14.47 |
-0.7241 |
0.469 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
6.19 |
1 |
4 |
1 |
6.04 |
3.76 |
4.79 |
5.58 |
7.31 |
4.96 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-52.36 |
143714 |
-0.0003644 |
0.9997 |
|
| aquaculture |
-104.1 |
2038169 |
-5.109e-05 |
1 |
|
| city |
35.37 |
2940960 |
1.203e-05 |
1 |
|
| dredging_collect |
-18.11 |
3776616 |
-4.796e-06 |
1 |
|
| dredging_dump |
-33.46 |
2851582 |
-1.173e-05 |
1 |
|
| industry |
80.68 |
479017 |
0.0001684 |
0.9999 |
|
| shipping_mooring |
-46.46 |
3580537 |
-1.298e-05 |
1 |
|
| shipping_traffic |
-11.27 |
681163 |
-1.654e-05 |
1 |
|
| sewers_rain |
60.22 |
3202956 |
1.88e-05 |
1 |
|
| sewers_waste |
-82.7 |
4527545 |
-1.827e-05 |
1 |
|
| wharves_city |
-12.23 |
4788895 |
-2.553e-06 |
1 |
|
| wharves_industry |
-0.6252 |
5946143 |
-1.052e-07 |
1 |
|
| fisheries_trap |
12.03 |
158705 |
7.582e-05 |
0.9999 |
|
| fisheries_trawl |
10.99 |
371397 |
2.959e-05 |
1 |
|
| fisheries_net |
3.301 |
126294 |
2.613e-05 |
1 |
|
| fisheries_dredge |
11.44 |
437581 |
2.614e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
28.8 |
36 |
56.7 |
42.9 |
9.56 |
38.8 |
10.9 |
42.9 |
57.6 |
66.6 |
89.9 |
4.06 |
2.62 |
1.34 |
4.41 |

Scoloplos sp
## SDM for: scoloplos_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Serripes groenlandicus
## SDM for: serripes_groenlandicus
Abiotic parameters
## McFadden's pseudo-R2 is: -2.79
## Tjur's pseudo-R2 is: 0.5
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.57e+15 |
7978909 |
-196769587 |
0 |
* * * |
| om |
-3.245e+14 |
14257309 |
-22760563 |
0 |
* * * |
| gravel |
2.134e+14 |
8714549 |
24491989 |
0 |
* * * |
| silt |
-5.273e+13 |
15274819 |
-3451885 |
0 |
* * * |
| clay |
-1.581e+15 |
21858102 |
-72321094 |
0 |
* * * |
| arsenic |
-6.07e+13 |
11951982 |
-5078852 |
0 |
* * * |
| cadmium |
1.173e+14 |
10400421 |
11282206 |
0 |
* * * |
| copper |
3.939e+14 |
13687347 |
28778234 |
0 |
* * * |
| iron |
-1.633e+14 |
9620115 |
-16972757 |
0 |
* * * |
| manganese |
-4.656e+13 |
14288707 |
-3258821 |
0 |
* * * |
| mercury |
1.036e+14 |
12068606 |
8584835 |
0 |
* * * |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-559.1 |
6362172 |
-8.787e-05 |
0.9999 |
|
| aquaculture |
699.9 |
31262734 |
2.239e-05 |
1 |
|
| city |
635.2 |
33661978 |
1.887e-05 |
1 |
|
| dredging_collect |
-664.6 |
39738271 |
-1.673e-05 |
1 |
|
| dredging_dump |
-663.3 |
29581682 |
-2.242e-05 |
1 |
|
| industry |
135.3 |
13310113 |
1.017e-05 |
1 |
|
| shipping_mooring |
540.3 |
31201517 |
1.732e-05 |
1 |
|
| shipping_traffic |
-460.3 |
16566389 |
-2.778e-05 |
1 |
|
| sewers_rain |
-912.6 |
34642702 |
-2.634e-05 |
1 |
|
| sewers_waste |
879.3 |
40975925 |
2.146e-05 |
1 |
|
| wharves_city |
-639.6 |
25283511 |
-2.53e-05 |
1 |
|
| wharves_industry |
1887 |
64272730 |
2.937e-05 |
1 |
|
| fisheries_trap |
-48.53 |
7046970 |
-6.887e-06 |
1 |
|
| fisheries_trawl |
135.1 |
2782398 |
4.857e-05 |
1 |
|
| fisheries_net |
140.8 |
7061711 |
1.994e-05 |
1 |
|
| fisheries_dredge |
-73.28 |
19129317 |
-3.831e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
421 |
103 |
163 |
122 |
130 |
350 |
162 |
50.2 |
185 |
20.2 |
232 |
1.11 |
118 |
1.09 |
1.78 |

Sipuncula
## SDM for: sipuncula
Abiotic parameters
## McFadden's pseudo-R2 is: 0.2
## Tjur's pseudo-R2 is: 0.14
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-11.04 |
1228 |
-0.008987 |
0.9928 |
|
| om |
-0.06194 |
0.6334 |
-0.09778 |
0.9221 |
|
| gravel |
-30.31 |
4384 |
-0.006913 |
0.9945 |
|
| silt |
1.606 |
0.9186 |
1.748 |
0.08043 |
|
| clay |
-0.9246 |
1.502 |
-0.6157 |
0.5381 |
|
| arsenic |
-0.215 |
0.4955 |
-0.4339 |
0.6644 |
|
| cadmium |
-0.003982 |
0.6238 |
-0.006384 |
0.9949 |
|
| copper |
0.9447 |
0.9321 |
1.014 |
0.3108 |
|
| iron |
-1.095 |
1.373 |
-0.797 |
0.4254 |
|
| manganese |
0.02073 |
0.7768 |
0.02669 |
0.9787 |
|
| mercury |
-0.753 |
0.7264 |
-1.037 |
0.2999 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.89 |
1 |
1.96 |
1.04 |
1.62 |
1.64 |
2.88 |
3.12 |
2.14 |
1.7 |

Influence indices
## McFadden's pseudo-R2 is: 0.19
## Tjur's pseudo-R2 is: 0.17
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-63.64 |
9900 |
-0.006429 |
0.9949 |
|
| aquaculture |
-1.972 |
3.528 |
-0.5591 |
0.5761 |
|
| city |
-5.898 |
5.934 |
-0.9939 |
0.3203 |
|
| dredging_collect |
0.5101 |
2.952 |
0.1728 |
0.8628 |
|
| dredging_dump |
3.151 |
3.309 |
0.9524 |
0.3409 |
|
| industry |
2.518 |
1.567 |
1.607 |
0.108 |
|
| shipping_mooring |
1.088 |
3.567 |
0.3051 |
0.7603 |
|
| shipping_traffic |
-1.256 |
1.784 |
-0.7041 |
0.4814 |
|
| sewers_rain |
4.471 |
3.685 |
1.213 |
0.225 |
|
| sewers_waste |
-4.955 |
4.74 |
-1.045 |
0.2959 |
|
| wharves_city |
4.911 |
5.873 |
0.8362 |
0.403 |
|
| wharves_industry |
-5.526 |
5.165 |
-1.07 |
0.2847 |
|
| fisheries_trap |
0.1574 |
0.3524 |
0.4468 |
0.655 |
|
| fisheries_trawl |
-1.243 |
1.586 |
-0.784 |
0.433 |
|
| fisheries_net |
-634.2 |
102651 |
-0.006178 |
0.9951 |
|
| fisheries_dredge |
-0.143 |
0.9339 |
-0.1531 |
0.8783 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
11.3 |
14.3 |
6.86 |
8.03 |
3.17 |
11.1 |
4.93 |
11.3 |
15.3 |
15 |
12.3 |
1.24 |
1.5 |
1 |
1.72 |

Solamen glandula
## SDM for: solamen_glandula
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Solariella sp
## SDM for: solariella_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0.63
## Tjur's pseudo-R2 is: 0.57
## Pearson's pseudo-R2 is: 0.56
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-15.78 |
8.372 |
-1.885 |
0.0594 |
|
| om |
-2.211 |
1.785 |
-1.238 |
0.2156 |
|
| gravel |
1.236 |
0.8289 |
1.491 |
0.1358 |
|
| silt |
4.686 |
2.543 |
1.843 |
0.06532 |
|
| clay |
-20.23 |
17.58 |
-1.151 |
0.2498 |
|
| arsenic |
2.721 |
3.381 |
0.8049 |
0.4209 |
|
| cadmium |
-2.661 |
1.976 |
-1.347 |
0.1781 |
|
| copper |
1.346 |
1.635 |
0.8232 |
0.4104 |
|
| iron |
-0.8998 |
2.432 |
-0.37 |
0.7114 |
|
| manganese |
-6.382 |
7.393 |
-0.8632 |
0.388 |
|
| mercury |
-8.379 |
4.247 |
-1.973 |
0.04852 |
* |
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.82 |
1.39 |
4.1 |
1.34 |
1.55 |
2.01 |
1.42 |
2.21 |
2.72 |
3.44 |

Influence indices
## McFadden's pseudo-R2 is: 0.61
## Tjur's pseudo-R2 is: 0.52
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-133.2 |
14038 |
-0.00949 |
0.9924 |
|
| aquaculture |
-44.82 |
27.08 |
-1.655 |
0.09795 |
|
| city |
-200.2 |
198.8 |
-1.007 |
0.3138 |
|
| dredging_collect |
3.86 |
13.36 |
0.2889 |
0.7726 |
|
| dredging_dump |
-20.27 |
42.16 |
-0.4808 |
0.6307 |
|
| industry |
-7.981 |
13.5 |
-0.5911 |
0.5545 |
|
| shipping_mooring |
-24.27 |
32.46 |
-0.7474 |
0.4548 |
|
| shipping_traffic |
-9.323 |
8.823 |
-1.057 |
0.2906 |
|
| sewers_rain |
60.97 |
45.29 |
1.346 |
0.1782 |
|
| sewers_waste |
-112.6 |
67.14 |
-1.678 |
0.09338 |
|
| wharves_city |
226.7 |
223.8 |
1.013 |
0.311 |
|
| wharves_industry |
13.36 |
21.06 |
0.6342 |
0.526 |
|
| fisheries_trap |
-2.575 |
3.026 |
-0.851 |
0.3948 |
|
| fisheries_trawl |
3.667 |
3.425 |
1.071 |
0.2843 |
|
| fisheries_net |
-679.2 |
145560 |
-0.004666 |
0.9963 |
|
| fisheries_dredge |
-3.624 |
2.652 |
-1.366 |
0.1719 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
25.8 |
334 |
27.5 |
72.6 |
21.1 |
37.4 |
15.6 |
53.8 |
63.3 |
387 |
37.8 |
2.32 |
6.36 |
1 |
3.33 |

Strongylocentrotus sp
## SDM for: strongylocentrotus_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-253.2 |
569872 |
-0.0004443 |
0.9996 |
|
| om |
-146 |
346106 |
-0.0004218 |
0.9997 |
|
| gravel |
27.65 |
96054 |
0.0002879 |
0.9998 |
|
| silt |
119.1 |
306874 |
0.000388 |
0.9997 |
|
| clay |
-377.1 |
1959930 |
-0.0001924 |
0.9998 |
|
| arsenic |
25.65 |
237382 |
0.000108 |
0.9999 |
|
| cadmium |
-55.59 |
190996 |
-0.0002911 |
0.9998 |
|
| copper |
63.14 |
497712 |
0.0001269 |
0.9999 |
|
| iron |
12.9 |
108119 |
0.0001193 |
0.9999 |
|
| manganese |
0.1801 |
556386 |
3.236e-07 |
1 |
|
| mercury |
-132 |
399118 |
-0.0003308 |
0.9997 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.19 |
2.78 |
5.98 |
2.23 |
3.11 |
3.52 |
9.64 |
2.4 |
8.71 |
4.38 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-48.06 |
75858 |
-0.0006335 |
0.9995 |
|
| aquaculture |
-18.14 |
897599 |
-2.021e-05 |
1 |
|
| city |
-36.02 |
627166 |
-5.743e-05 |
1 |
|
| dredging_collect |
-9.82 |
404726 |
-2.426e-05 |
1 |
|
| dredging_dump |
-7.461 |
459611 |
-1.623e-05 |
1 |
|
| industry |
-2.274 |
398214 |
-5.711e-06 |
1 |
|
| shipping_mooring |
-50.52 |
538479 |
-9.381e-05 |
0.9999 |
|
| shipping_traffic |
7.258 |
183375 |
3.958e-05 |
1 |
|
| sewers_rain |
-81.01 |
789813 |
-0.0001026 |
0.9999 |
|
| sewers_waste |
100.1 |
1060232 |
9.438e-05 |
0.9999 |
|
| wharves_city |
57.74 |
826509 |
6.986e-05 |
0.9999 |
|
| wharves_industry |
14.73 |
655559 |
2.247e-05 |
1 |
|
| fisheries_trap |
-22.82 |
296552 |
-7.695e-05 |
0.9999 |
|
| fisheries_trawl |
10.73 |
50677 |
0.0002118 |
0.9998 |
|
| fisheries_net |
6.716 |
74033 |
9.072e-05 |
0.9999 |
|
| fisheries_dredge |
-3.998 |
166667 |
-2.399e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
18.6 |
18.4 |
12.4 |
14.6 |
11.3 |
13.7 |
3.57 |
11.2 |
18.5 |
23.9 |
18.8 |
2.32 |
2.85 |
1.31 |
1.92 |

Tachyrhynchus erosus
## SDM for: tachyrhynchus_erosus
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Thracia septentrionalis
## SDM for: thracia_septentrionalis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.13
## Tjur's pseudo-R2 is: 0.14
## Pearson's pseudo-R2 is: 0.16
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.713 |
0.4005 |
-4.277 |
1.896e-05 |
* * * |
| om |
-0.8216 |
0.6204 |
-1.324 |
0.1854 |
|
| gravel |
0.1418 |
0.3199 |
0.4434 |
0.6575 |
|
| silt |
0.3665 |
0.5504 |
0.6659 |
0.5055 |
|
| clay |
0.2645 |
0.6859 |
0.3856 |
0.6998 |
|
| arsenic |
-0.03919 |
0.6671 |
-0.05874 |
0.9532 |
|
| cadmium |
0.2923 |
0.411 |
0.7113 |
0.4769 |
|
| copper |
-0.02557 |
0.6218 |
-0.04112 |
0.9672 |
|
| iron |
-0.3614 |
0.6353 |
-0.5689 |
0.5694 |
|
| manganese |
0.1917 |
0.7576 |
0.253 |
0.8003 |
|
| mercury |
-0.9035 |
0.7035 |
-1.284 |
0.199 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.61 |
1.2 |
1.81 |
1.13 |
1.6 |
1.41 |
1.94 |
1.68 |
1.97 |
1.49 |

Influence indices
## McFadden's pseudo-R2 is: 0.25
## Tjur's pseudo-R2 is: 0.26
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-54.1 |
5800 |
-0.009328 |
0.9926 |
|
| aquaculture |
9.15 |
4.447 |
2.058 |
0.03962 |
* |
| city |
11.16 |
4.222 |
2.642 |
0.008238 |
* * |
| dredging_collect |
-0.2449 |
3.055 |
-0.08017 |
0.9361 |
|
| dredging_dump |
-3.418 |
2.791 |
-1.224 |
0.2208 |
|
| industry |
-0.8043 |
1.38 |
-0.5827 |
0.5601 |
|
| shipping_mooring |
5.303 |
2.869 |
1.848 |
0.06458 |
|
| shipping_traffic |
1.733 |
1.282 |
1.352 |
0.1765 |
|
| sewers_rain |
-4.03 |
3.679 |
-1.095 |
0.2733 |
|
| sewers_waste |
4.944 |
5.029 |
0.9832 |
0.3255 |
|
| wharves_city |
-12.05 |
4.717 |
-2.555 |
0.01061 |
* |
| wharves_industry |
2.788 |
4.696 |
0.5936 |
0.5528 |
|
| fisheries_trap |
0.726 |
0.3127 |
2.321 |
0.02026 |
* |
| fisheries_trawl |
-0.2674 |
0.3331 |
-0.8029 |
0.422 |
|
| fisheries_net |
-536.4 |
60137 |
-0.008919 |
0.9929 |
|
| fisheries_dredge |
-2.448 |
1.672 |
-1.464 |
0.1433 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
15.1 |
13.8 |
10 |
8.97 |
4.97 |
9.03 |
4.52 |
11.3 |
16.3 |
14.4 |
15.5 |
1.37 |
1.47 |
1 |
2.55 |

Thyasira gouldi
## SDM for: thyasira_gouldi
Abiotic parameters
## McFadden's pseudo-R2 is: 0.19
## Tjur's pseudo-R2 is: 0.21
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-1.937 |
0.972 |
-1.993 |
0.04627 |
* |
| om |
1.1 |
0.5836 |
1.884 |
0.05954 |
|
| gravel |
-0.2151 |
0.3224 |
-0.667 |
0.5048 |
|
| silt |
-0.09162 |
0.5824 |
-0.1573 |
0.875 |
|
| clay |
-4.1 |
5.058 |
-0.8106 |
0.4176 |
|
| arsenic |
0.1166 |
0.5209 |
0.2237 |
0.823 |
|
| cadmium |
-1.434 |
0.6438 |
-2.227 |
0.02594 |
* |
| copper |
0.1666 |
0.7501 |
0.222 |
0.8243 |
|
| iron |
-1.209 |
0.8336 |
-1.45 |
0.147 |
|
| manganese |
0.09805 |
0.7507 |
0.1306 |
0.8961 |
|
| mercury |
0.2309 |
0.5034 |
0.4587 |
0.6464 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.2 |
1.33 |
2.21 |
1.14 |
1.65 |
1.95 |
2.55 |
2.3 |
2.64 |
1.84 |

Influence indices
## McFadden's pseudo-R2 is: 0.35
## Tjur's pseudo-R2 is: 0.36
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-95.62 |
9421 |
-0.01015 |
0.9919 |
|
| aquaculture |
-5.991 |
3.543 |
-1.691 |
0.09081 |
|
| city |
-20.51 |
13.22 |
-1.552 |
0.1207 |
|
| dredging_collect |
10.22 |
4.15 |
2.463 |
0.01376 |
* |
| dredging_dump |
2.973 |
3.365 |
0.8835 |
0.3769 |
|
| industry |
3.481 |
1.621 |
2.148 |
0.03173 |
* |
| shipping_mooring |
8.818 |
5.28 |
1.67 |
0.09491 |
|
| shipping_traffic |
-0.3772 |
1.361 |
-0.2771 |
0.7817 |
|
| sewers_rain |
15.1 |
5.968 |
2.53 |
0.01141 |
* |
| sewers_waste |
-22.76 |
8.693 |
-2.619 |
0.008829 |
* * |
| wharves_city |
14.73 |
10.75 |
1.37 |
0.1707 |
|
| wharves_industry |
-16.4 |
6.847 |
-2.396 |
0.01659 |
* |
| fisheries_trap |
0.3183 |
0.4545 |
0.7003 |
0.4838 |
|
| fisheries_trawl |
-0.1167 |
0.3576 |
-0.3263 |
0.7442 |
|
| fisheries_net |
-967.5 |
97683 |
-0.009905 |
0.9921 |
|
| fisheries_dredge |
2.436 |
1.528 |
1.594 |
0.1109 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
13.3 |
34.5 |
12.4 |
9.74 |
3.59 |
18.9 |
3.94 |
19.3 |
30.5 |
29.9 |
19.8 |
1.16 |
1.46 |
1 |
2.8 |

Thyasira sp
## SDM for: thyasira_sp
Abiotic parameters
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
42341 |
-0.0006274 |
0.9995 |
|
| om |
9.706e-15 |
75659 |
1.283e-19 |
1 |
|
| gravel |
-2.813e-15 |
46245 |
-6.082e-20 |
1 |
|
| silt |
-9.501e-15 |
81058 |
-1.172e-19 |
1 |
|
| clay |
6.063e-15 |
115994 |
5.227e-20 |
1 |
|
| arsenic |
5.656e-15 |
63425 |
8.917e-20 |
1 |
|
| cadmium |
2.434e-15 |
55192 |
4.411e-20 |
1 |
|
| copper |
-1.264e-14 |
72634 |
-1.74e-19 |
1 |
|
| iron |
4.18e-15 |
51051 |
8.188e-20 |
1 |
|
| manganese |
-7.198e-15 |
75825 |
-9.492e-20 |
1 |
|
| mercury |
8.982e-15 |
64044 |
1.402e-19 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.96 |
1.33 |
2.14 |
1.09 |
1.56 |
1.44 |
1.91 |
1.39 |
1.85 |
1.56 |

Influence indices
## McFadden's pseudo-R2 is: 0
## Tjur's pseudo-R2 is: NaN
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-26.57 |
39784 |
-0.0006678 |
0.9995 |
|
| aquaculture |
-4.492e-15 |
354336 |
-1.268e-20 |
1 |
|
| city |
2.363e-15 |
321138 |
7.357e-21 |
1 |
|
| dredging_collect |
-2.952e-15 |
254116 |
-1.162e-20 |
1 |
|
| dredging_dump |
2.828e-15 |
294996 |
9.588e-21 |
1 |
|
| industry |
6.103e-15 |
160225 |
3.809e-20 |
1 |
|
| shipping_mooring |
-2.342e-15 |
271832 |
-8.617e-21 |
1 |
|
| shipping_traffic |
8.126e-15 |
121076 |
6.712e-20 |
1 |
|
| sewers_rain |
-4.695e-15 |
356417 |
-1.317e-20 |
1 |
|
| sewers_waste |
4.678e-15 |
479508 |
9.757e-21 |
1 |
|
| wharves_city |
8.617e-16 |
384550 |
2.241e-21 |
1 |
|
| wharves_industry |
-1.402e-14 |
419585 |
-3.34e-20 |
1 |
|
| fisheries_trap |
6.28e-16 |
38619 |
1.626e-20 |
1 |
|
| fisheries_trawl |
-3.839e-15 |
46815 |
-8.201e-20 |
1 |
|
| fisheries_net |
2.097e-17 |
38310 |
5.473e-22 |
1 |
|
| fisheries_dredge |
-1.4e-15 |
103236 |
-1.356e-20 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
9.47 |
8.46 |
6.53 |
7.68 |
4.32 |
6.88 |
3.23 |
9.16 |
12.4 |
9.99 |
10.9 |
1.08 |
1.35 |
1.11 |
1.66 |

Trichotropis bicarinata
## SDM for: trichotropis_bicarinata
Abiotic parameters
## McFadden's pseudo-R2 is: 0.16
## Tjur's pseudo-R2 is: 0.02
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-107.6 |
38257 |
-0.002812 |
0.9978 |
|
| om |
0.2478 |
2.756 |
0.08991 |
0.9284 |
|
| gravel |
-30.79 |
31433 |
-0.0009794 |
0.9992 |
|
| silt |
0.04291 |
2.451 |
0.0175 |
0.986 |
|
| clay |
-510.4 |
203220 |
-0.002512 |
0.998 |
|
| arsenic |
0.2016 |
3.626 |
0.05559 |
0.9557 |
|
| cadmium |
-0.2526 |
2.154 |
-0.1173 |
0.9066 |
|
| copper |
0.3548 |
2.141 |
0.1657 |
0.8684 |
|
| iron |
-0.07093 |
1.132 |
-0.06269 |
0.95 |
|
| manganese |
-1.669 |
5.303 |
-0.3148 |
0.7529 |
|
| mercury |
-1.39 |
3.01 |
-0.4617 |
0.6443 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
1.8 |
1 |
1.87 |
1 |
1.38 |
1.67 |
1.6 |
1.51 |
1.82 |
1.46 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-76.04 |
139169 |
-0.0005464 |
0.9996 |
|
| aquaculture |
-249 |
1551028 |
-0.0001605 |
0.9999 |
|
| city |
-159.9 |
2117161 |
-7.554e-05 |
0.9999 |
|
| dredging_collect |
-109.7 |
1596207 |
-6.872e-05 |
0.9999 |
|
| dredging_dump |
-46.45 |
1678940 |
-2.767e-05 |
1 |
|
| industry |
133.7 |
498328 |
0.0002684 |
0.9998 |
|
| shipping_mooring |
-190.8 |
2202337 |
-8.662e-05 |
0.9999 |
|
| shipping_traffic |
-42.27 |
1309807 |
-3.228e-05 |
1 |
|
| sewers_rain |
66.56 |
2123791 |
3.134e-05 |
1 |
|
| sewers_waste |
-71.06 |
3039533 |
-2.338e-05 |
1 |
|
| wharves_city |
274.8 |
3128180 |
8.784e-05 |
0.9999 |
|
| wharves_industry |
53.93 |
1331590 |
4.05e-05 |
1 |
|
| fisheries_trap |
-49.95 |
180585 |
-0.0002766 |
0.9998 |
|
| fisheries_trawl |
3.445 |
1351368 |
2.55e-06 |
1 |
|
| fisheries_net |
4.872 |
117130 |
4.16e-05 |
1 |
|
| fisheries_dredge |
-2.791 |
858084 |
-3.253e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
28.3 |
27.5 |
27.7 |
25.6 |
9.14 |
35.8 |
24.4 |
34.4 |
53.8 |
42.1 |
22.7 |
2.21 |
10.3 |
1.26 |
5.39 |

Turritellopsis stimpsoni
## SDM for: turritellopsis_stimpsoni
Abiotic parameters
## McFadden's pseudo-R2 is: 0.52
## Tjur's pseudo-R2 is: 0.36
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-190.7 |
81645 |
-0.002336 |
0.9981 |
|
| om |
6.819 |
5.27 |
1.294 |
0.1957 |
|
| gravel |
-1.005 |
1.007 |
-0.9974 |
0.3186 |
|
| silt |
-6.932 |
4.992 |
-1.389 |
0.165 |
|
| clay |
-990.2 |
445526 |
-0.002223 |
0.9982 |
|
| arsenic |
2.756 |
3.336 |
0.8262 |
0.4087 |
|
| cadmium |
-2.536 |
2.245 |
-1.13 |
0.2586 |
|
| copper |
2.35 |
2.328 |
1.009 |
0.3128 |
|
| iron |
-5.67 |
4.496 |
-1.261 |
0.2072 |
|
| manganese |
-3.814 |
4.669 |
-0.8168 |
0.4141 |
|
| mercury |
1.607 |
2.028 |
0.7924 |
0.4281 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
5.51 |
1.77 |
5.27 |
1 |
2.24 |
2.73 |
2.84 |
3.76 |
2.66 |
2.41 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-980.4 |
2402282 |
-0.0004081 |
0.9997 |
|
| aquaculture |
568 |
3216366 |
0.0001766 |
0.9999 |
|
| city |
2235 |
8062568 |
0.0002772 |
0.9998 |
|
| dredging_collect |
818.8 |
4001270 |
0.0002046 |
0.9998 |
|
| dredging_dump |
1251 |
3427008 |
0.0003651 |
0.9997 |
|
| industry |
-408.4 |
1738626 |
-0.0002349 |
0.9998 |
|
| shipping_mooring |
-555.1 |
11064629 |
-5.017e-05 |
1 |
|
| shipping_traffic |
349.4 |
1883953 |
0.0001854 |
0.9999 |
|
| sewers_rain |
1472 |
4521220 |
0.0003256 |
0.9997 |
|
| sewers_waste |
-1592 |
10779339 |
-0.0001477 |
0.9999 |
|
| wharves_city |
-2943 |
10350412 |
-0.0002843 |
0.9998 |
|
| wharves_industry |
-925.7 |
9048771 |
-0.0001023 |
0.9999 |
|
| fisheries_trap |
-646.5 |
4467910 |
-0.0001447 |
0.9999 |
|
| fisheries_trawl |
-79.34 |
690430 |
-0.0001149 |
0.9999 |
|
| fisheries_net |
-13.16 |
6464067 |
-2.035e-06 |
1 |
|
| fisheries_dredge |
4.467 |
288264 |
1.55e-05 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
43.6 |
131 |
73.1 |
41.5 |
27.6 |
154 |
32.7 |
109 |
191 |
138 |
126 |
10.2 |
11.3 |
1 |
2.56 |

Yoldia myalis
## SDM for: yoldia_myalis
Abiotic parameters
## McFadden's pseudo-R2 is: 0.07
## Tjur's pseudo-R2 is: 0.02
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-347.3 |
90666 |
-0.003831 |
0.9969 |
|
| om |
0.4722 |
1.254 |
0.3765 |
0.7065 |
|
| gravel |
-0.02457 |
0.5421 |
-0.04533 |
0.9638 |
|
| silt |
-0.6866 |
1.217 |
-0.5644 |
0.5725 |
|
| clay |
-1877 |
494750 |
-0.003793 |
0.997 |
|
| arsenic |
0.1898 |
1.177 |
0.1612 |
0.8719 |
|
| cadmium |
-0.216 |
0.9361 |
-0.2308 |
0.8175 |
|
| copper |
-0.3942 |
1.33 |
-0.2963 |
0.767 |
|
| iron |
-0.4075 |
1.106 |
-0.3686 |
0.7124 |
|
| manganese |
0.4142 |
1.367 |
0.303 |
0.7619 |
|
| mercury |
0.1164 |
1.028 |
0.1133 |
0.9098 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
2.18 |
1.63 |
2.16 |
1 |
1.58 |
1.45 |
2.17 |
1.63 |
2.45 |
1.81 |

Influence indices
## McFadden's pseudo-R2 is: 1
## Tjur's pseudo-R2 is: 1
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
| (Intercept) |
-542 |
1844508 |
-0.0002939 |
0.9998 |
|
| aquaculture |
-252.7 |
13894638 |
-1.819e-05 |
1 |
|
| city |
-381.4 |
19798867 |
-1.927e-05 |
1 |
|
| dredging_collect |
-735.9 |
17800592 |
-4.134e-05 |
1 |
|
| dredging_dump |
48.62 |
14174691 |
3.43e-06 |
1 |
|
| industry |
381.4 |
4913263 |
7.762e-05 |
0.9999 |
|
| shipping_mooring |
501.4 |
18081853 |
2.773e-05 |
1 |
|
| shipping_traffic |
197.8 |
2449158 |
8.074e-05 |
0.9999 |
|
| sewers_rain |
582.1 |
4387610 |
0.0001327 |
0.9999 |
|
| sewers_waste |
-1324 |
3699990 |
-0.0003579 |
0.9997 |
|
| wharves_city |
56.61 |
18776123 |
3.015e-06 |
1 |
|
| wharves_industry |
427 |
32498535 |
1.314e-05 |
1 |
|
| fisheries_trap |
-84.43 |
3438724 |
-2.455e-05 |
1 |
|
| fisheries_trawl |
-300.3 |
4677720 |
-6.421e-05 |
0.9999 |
|
| fisheries_net |
21.64 |
6461332 |
3.349e-06 |
1 |
|
| fisheries_dredge |
-26.96 |
5328338 |
-5.061e-06 |
1 |
|
## No RMSE calculation available for logistic models
Variance Inflation Factors
| VIF |
258 |
252 |
193 |
162 |
35.2 |
293 |
30.5 |
54.8 |
56.8 |
263 |
354 |
17.5 |
7.98 |
1 |
45.2 |
